diff --git a/book/_toc.yml b/book/_toc.yml
index c88c8488..2d482cb2 100644
--- a/book/_toc.yml
+++ b/book/_toc.yml
@@ -159,7 +159,6 @@ parts:
       - file: signal/videos
         title: Supplementary Videos
 
-    # START REMOVE-FROM-PUBLISH
     - file: time_series/intro.md
       title: Time Series Analysis
       sections:
@@ -183,11 +182,6 @@ parts:
         - file: time_series/ar_exercise.ipynb
       - file: time_series/forecasting
         title: "Time Series forecasting"
-      # START REMOVE-FROM-PUBLISH
-      - file: time_series/notebook
-        title: Notebook
-      # END REMOVE-FROM-PUBLISH
-    # END REMOVE-FROM-PUBLISH
 
     # START REMOVE-FROM-PUBLISH
     - file: optimization/overview
diff --git a/book/intro.md b/book/intro.md
index 07dd7122..181201ce 100644
--- a/book/intro.md
+++ b/book/intro.md
@@ -1,4 +1,4 @@
-# Welcome to the MUDE Textbook
+# Welcome to the MUDE Textbook test
 
 Welcome to the MUDE textbook for the 2024-25 academic year. This is where assigned reading is located, along with interactive exercises to practice and study the module material.
 
diff --git a/book/time_series/acf.md b/book/time_series/acf.md
index 3cd7a623..ab6659d1 100644
--- a/book/time_series/acf.md
+++ b/book/time_series/acf.md
@@ -3,7 +3,7 @@
 
 Before we can look into the modelling of a stochastic process using an Autoregressive (AR) model, we first need to introduce the autocovariance function (ACF) for a stationary time series, and describe the relationship between ACF and a power spectral density (PSD).
 
-As in the Chapter on #TODO (add reference to obs theory), the variance component is often determined based on the precision of an observation (at a given epoch), and the covariance components quantitatively indicate the statistical dependence (or independence) between observations. In this case, dependence is inherently introduced by the physical processes that produce the signal (of which our time series is a sample), and in fact our time series methods seek to (mathematically) account for this.
+As in [Observation theory](../observation_theory/01_Introduction.md), the variance component is often determined based on the precision of an observation (at a given epoch), and the covariance components quantitatively indicate the statistical dependence (or independence) between observations. In this case, dependence is inherently introduced by the physical processes that produce the signal (of which our time series is a sample), and in fact our time series methods seek to (mathematically) account for this.
 
 ## Autocovariance and autocorrelation
 
@@ -12,7 +12,7 @@ Let us assume an arbitrary (discrete) stationary time series, $S=[S_1,S_2,...,S_
 The *formal* (or: theoretical) autocovariance is defined as
 
 $$
-Cov(S_t, S_{t-\tau}) =\mathbb{E}(S_tS_{t-\tau})-\mu^2
+Cov(S_t, S_{t+\tau}) =\mathbb{E}(S_tS_{t+\tau})-\mu^2
 =c_{\tau}
 $$
 
@@ -23,7 +23,7 @@ We have that $Cov(S_t, S_{t-\tau}) =Cov(S_t, S_{t+\tau})$.
 
 Show that the covariance can be written as: 
 
-$$Cov(S_t, S_{t-\tau}) = \mathbb{E}(S_tS_{t-\tau})-\mu^2
+$$Cov(S_t, S_{t+\tau}) = \mathbb{E}(S_tS_{t+\tau})-\mu^2
 =c_{\tau}$$ 
 
 
@@ -31,12 +31,12 @@ $$Cov(S_t, S_{t-\tau}) = \mathbb{E}(S_tS_{t-\tau})-\mu^2
 :class: tip, dropdown
 
 $$
- Cov(S_t, S_{t-\tau})= \mathbb{E}[(S_t - \mathbb{E}(S_t))(S_{t-\tau} - \mathbb{E}(S_{t-\tau}))]\\
- = \mathbb{E}((S_t-\mu)(S_{t-\tau}-\mu))\\
- = \mathbb{E}(S_tS_{t-\tau} - \mu S_{t-\tau} - \mu S_t + \mu^2)\\
- = \mathbb{E}(S_tS_{t-\tau}) - \mu \mathbb{E}(S_{t-\tau}) - \mu \mathbb{E}(S_t) + \mu^2\\
-= \mathbb{E}(S_tS_{t-\tau}) - 2\mu^2 + \mu^2\\
-= \mathbb{E}(S_tS_{t-\tau}) - \mu^2\\
+ Cov(S_t, S_{t+\tau})= \mathbb{E}[(S_t - \mathbb{E}(S_t))(S_{t+\tau} - \mathbb{E}(S_{t+\tau}))]\\
+ = \mathbb{E}((S_t-\mu)(S_{t+\tau}-\mu))\\
+ = \mathbb{E}(S_tS_{t+\tau} - \mu S_{t+\tau} - \mu S_t + \mu^2)\\
+ = \mathbb{E}(S_tS_{t+\tau}) - \mu \mathbb{E}(S_{t+\tau}) - \mu \mathbb{E}(S_t) + \mu^2\\
+= \mathbb{E}(S_tS_{t+\tau}) - 2\mu^2 + \mu^2\\
+= \mathbb{E}(S_tS_{t+\tau}) - \mu^2\\
 $$
 ````
 :::
@@ -46,12 +46,14 @@ $$
 Prove that $Cov(S_t, S_{t-\tau}) =Cov(S_t, S_{t+\tau})$: 
 
 
-
 ````{admonition} Solution
 :class: tip, dropdown
 
 From the definition of covariance, we know that
-$$ Cov(a,b) = Cov(b,a)$$
+
+$$
+Cov(a,b) = Cov(b,a)
+$$
 
 Hence, we have that
 
@@ -74,7 +76,7 @@ $$ Cov(S_t, S_{t-\tau}) = Cov(S_t, S_{t+\tau})$$
 The *formal* autocorrelation is defined as
 
 $$
-r_{\tau} = \mathbb{E}(S_tS_{t-\tau})
+r_{\tau} = \mathbb{E}(S_tS_{t+\tau})
 $$
 
 ```{note}
@@ -262,42 +264,3 @@ $$
 
 ```
 :::
-
-<!-- ## Power spectral density
-
-The power spectral density (PSD) explains how the power (variance) of the signal is distributed over different frequencies. For instance, the PSD of a pure sine wave is flat *except* at its constituent frequency, where it will show a peak. Purely random noise has a flat power spectrum, indicating that all frequencies have an identical contribution to the variance of the signal! -->
-
-<!-- The power spectral density (PSD) explains how the power (variance) of the signal is distributed over different frequencies. For instance, the PSD of a pure sine wave is flat *except* at its constituent frequency, where it will show a peak. Purely random noise has a flat power spectrum, indicating that all frequencies have an identical contribution to the variance of the signal!
-
-### PSD vs ACF
-
-Knowledge on ACF, in time domain, is mathematically equivalent to knowledge on PSD, in the frequency domain, and vice-versa. And, from here, you might have a clue of where this is taking us... The PSD is the **discrete Fourier transform (DFT)** of the ACF.
-
-$$
-\begin{align*}
-\text{DFT}(\hat{c}_{\tau})&=\text{DFT}\left(\frac{1}{m}\sum_{i=1}^m s_i s_{i+\tau}\right)\\&=\frac{1}{m\Delta t}F_S(k) F_S(k)^*\\&=\frac{1}{m\Delta t}|F_S(k)|^2
-\end{align*}$$
-
-where the Fourier coefficients (see [DFT section](FFT)) are:
-
-$$F_S(k)  = \Delta t\sum_{i=1}^my_ie^{-j\frac{2\pi}{m}(k-1)(i-1)}$$
-
-```{note}
-In signal processing, it is common to write a sampled (discrete) signal as a small letter $s_i$ and the Fourier coefficients with capitals $S_k$. Since we also use capitals to indicate that $S$ is a random variable, we describe the DFT here for a realization $s_i$ of $S_i$, and use the notation $F_S(k)$ for the Fourier coefficients.
-```
-
-Conversely, the inverse discrete Fourier transform (IDFT) of the PSD is the ACF, so
-
-$$\text{IDFT}(F_{S}(k))=\hat{c}_{\tau}, \hspace{35px} \tau = 1,...,m \hspace{5px}\text{and}\hspace{5px} k = 1,...,m$$
-
-```{figure} ./figs/ACF_PSD.png
----
-height: 300px
-name: ACF_PSD
----
-Time series data, autocovariance and its power spectral density plots of white noise above and colored noise (not purely random) below.
-```
-
-The PSD explains how the power (variance) of the signal is distributed over different frequencies. The PSD of a pure sine wave is flat except at its constituent frequency.
-Purey random noise (i.e., white noise) has a flat power, indicating that all frequencies have identical contribution in making the variance of the signal. This is however not the case for time-correlated noise because different frequencies have different power values in making the total signal variability.
- -->
diff --git a/book/time_series/ar_exercise.ipynb b/book/time_series/ar_exercise.ipynb
index 53e23db4..86f38cd6 100644
--- a/book/time_series/ar_exercise.ipynb
+++ b/book/time_series/ar_exercise.ipynb
@@ -10,18 +10,274 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": 8,
+   "metadata": {
+    "tags": [
+     "auto-execute-page",
+     "thebe-remove-input-init"
+    ]
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n"
+    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
+    "\n",
+    "data = np.array([ 0.00000000e+00,  0.00000000e+00,  7.39366373e-01,  8.82012627e-01,\n",
+    "       -2.17941679e-01,  1.54974089e+00, -8.98443756e-02,  1.00141169e+00,\n",
+    "        2.42524470e-01, -1.02361298e-01, -1.10553118e+00,  3.04987823e-01,\n",
+    "       -8.69761112e-01, -1.11315875e+00, -4.24734667e-01,  1.45309595e-01,\n",
+    "        5.42097653e-01,  3.77506030e-01, -7.33921297e-01, -2.31148958e-01,\n",
+    "       -1.76689101e+00, -6.79235565e-01, -4.00350747e-01, -2.35241349e-01,\n",
+    "       -9.07637426e-01,  2.13326549e-01,  2.18065416e-01, -1.60757882e+00,\n",
+    "       -2.07263765e+00, -1.18111332e+00, -1.47885555e+00, -1.89930627e+00,\n",
+    "       -1.57038634e+00, -2.07430124e+00, -1.03020905e+00, -1.05763683e+00,\n",
+    "       -7.48698592e-01, -1.57544551e+00, -2.50491924e+00, -1.42908722e+00,\n",
+    "       -1.08312118e+00, -1.80363252e+00, -1.04630764e+00, -1.71038000e+00,\n",
+    "       -1.71349481e+00, -1.44771288e+00,  4.20877715e-01,  5.07187528e-01,\n",
+    "       -4.87173992e-01,  3.24049379e-01,  2.59529792e-01,  5.04614412e-01,\n",
+    "        4.73901121e-01,  2.72281199e-01, -1.41652577e+00, -1.39176422e+00,\n",
+    "       -8.29335646e-01, -4.20523393e-01, -8.41985423e-02,  9.41730115e-02,\n",
+    "       -1.50729414e+00, -8.74161867e-01,  2.01810837e-01, -2.17818122e+00,\n",
+    "       -5.92488929e-01,  1.07042695e+00,  4.37509451e-01,  7.22526958e-01,\n",
+    "        1.22407607e+00,  7.70631751e-01,  1.50808058e+00, -8.50142466e-01,\n",
+    "       -3.73831479e-01,  1.35391870e+00,  2.18917967e+00,  2.57638726e+00,\n",
+    "        2.70701733e+00,  1.36794873e+00,  1.29999210e+00,  1.91501107e+00,\n",
+    "        1.59349872e+00,  1.97732807e+00,  2.49992433e+00,  1.93175636e+00,\n",
+    "        1.47660557e+00,  1.17536071e+00,  1.39557491e+00,  1.44857054e+00,\n",
+    "        7.89092903e-01, -6.46392931e-01,  2.95521216e-01, -1.08968239e-01,\n",
+    "       -3.85438702e-01, -3.58086137e-01, -5.09981434e-01, -2.05496939e+00,\n",
+    "       -3.64585529e-01, -3.96458235e-02,  3.53929808e-01,  1.58901411e+00,\n",
+    "        9.53132950e-02,  7.67470550e-01,  1.15384857e+00,  7.96439922e-01,\n",
+    "        9.76627268e-01,  3.47042888e-01,  1.00590245e+00,  1.50082738e-01,\n",
+    "        2.37379710e-01,  1.96873095e+00,  2.20072042e+00,  1.56046852e+00,\n",
+    "        6.64436129e-01,  2.96544445e-01,  1.72227831e+00,  1.10644286e+00,\n",
+    "        1.63238235e+00,  6.63026552e-01,  3.61978366e-01,  6.45668467e-01,\n",
+    "        1.27851822e+00,  2.65148989e+00,  9.99420989e-01,  3.58085372e-01,\n",
+    "       -4.62291606e-01,  9.37518000e-01,  1.23164122e+00, -3.12475856e-01,\n",
+    "        1.36065286e-01, -6.16493968e-02,  6.69941343e-01, -3.42736465e-01,\n",
+    "       -1.34703063e+00, -1.67947236e+00,  5.69358500e-01,  5.83468985e-01,\n",
+    "        7.41796103e-01,  6.39326772e-01,  2.10445565e-01,  3.92168977e-01,\n",
+    "        6.40725851e-01,  8.99547148e-01,  1.00500576e+00, -8.71818439e-02,\n",
+    "        4.04000299e-02,  8.70113665e-01, -1.44264848e-01,  5.39213359e-01,\n",
+    "        6.66901278e-01,  8.72123995e-01,  1.84825136e+00,  1.10125095e+00,\n",
+    "        9.95721987e-01,  6.26088972e-01,  7.62512607e-01,  1.16511511e-01,\n",
+    "        4.22571492e-01,  9.89100398e-02,  5.49712966e-01, -1.56725388e-01,\n",
+    "        4.27536063e-01,  2.05360843e-01, -5.64858058e-01, -2.69873034e-02,\n",
+    "       -9.37568737e-01,  8.65612664e-01, -3.69365069e-01,  2.39337297e+00,\n",
+    "        8.60106273e-01,  3.29178916e+00,  1.71736403e+00,  2.13874664e+00,\n",
+    "        1.48955823e+00,  1.48006688e+00, -6.88803421e-01, -3.24234835e-01,\n",
+    "       -1.24331549e+00, -6.66509456e-01, -9.04246649e-01, -1.90451608e+00,\n",
+    "       -9.87961104e-01, -5.48066393e-01, -1.57398280e-01, -4.07591149e-01,\n",
+    "        1.15083345e+00,  5.88506643e-01, -5.92900945e-01, -3.44438570e-01,\n",
+    "       -7.29310067e-01,  5.74169750e-01,  4.04520673e-01, -2.20288636e-01,\n",
+    "       -2.77120327e-02,  9.88213495e-02, -1.09190567e+00,  7.19825871e-02,\n",
+    "       -9.84122442e-01, -1.80535953e-01, -5.14311755e-01, -6.47274166e-01,\n",
+    "       -7.56479222e-01,  1.08118071e-01, -9.57690388e-01, -1.57851451e+00,\n",
+    "       -1.31524418e+00, -1.40305337e+00, -2.54378176e+00, -1.84970651e+00,\n",
+    "       -2.32341947e+00, -1.60528868e+00, -6.02478175e-01, -1.51805873e+00,\n",
+    "       -6.37833606e-01,  2.76604125e-01, -2.33841731e-01, -1.36818872e+00,\n",
+    "       -5.72040718e-01,  6.55878864e-01,  1.13125989e+00,  2.20828257e+00,\n",
+    "        1.08565693e+00,  1.28351412e-01,  5.46169855e-01,  5.93641545e-01,\n",
+    "        6.04036418e-01,  3.49137797e-01, -1.83183726e-01,  6.16385128e-01,\n",
+    "        2.10314828e-01,  3.45091013e-01, -6.13021032e-01,  1.26537948e+00,\n",
+    "        7.35755004e-01, -1.91878585e-01, -1.02143047e+00, -8.41844559e-01,\n",
+    "       -9.04793546e-01,  9.84542133e-01,  4.52293503e-01, -8.17515258e-01,\n",
+    "       -2.38201270e-01,  2.09588612e-01,  3.10936455e-01, -6.13386042e-02,\n",
+    "        4.45269097e-01, -1.22797128e+00, -2.25841101e-01,  6.68332290e-01,\n",
+    "        2.14404817e+00,  6.70914150e-01,  1.79696828e+00,  2.49805512e+00,\n",
+    "        3.64554459e-01, -2.28644444e-01, -1.09699150e+00, -1.03731317e+00,\n",
+    "       -3.96379156e-01, -1.38065556e+00, -3.82528623e-01, -1.02683833e+00,\n",
+    "       -1.51626243e+00, -3.08281555e-02, -1.61811653e-01, -3.08337884e-01,\n",
+    "        6.78369204e-02, -1.07006199e+00, -5.79365880e-01,  2.51602954e-01,\n",
+    "       -1.39343464e+00, -1.05284048e+00, -1.06705460e+00, -4.86584542e-01,\n",
+    "       -2.45306112e+00, -1.49506393e+00, -2.64593592e+00, -3.01740934e+00,\n",
+    "       -1.77716519e+00, -2.71373930e+00, -2.03803161e+00, -1.20648230e+00,\n",
+    "       -1.03999481e+00, -9.91418930e-01, -9.91249144e-01, -8.44261622e-01,\n",
+    "       -1.50101351e+00, -1.25887947e-01, -1.40571889e+00,  4.88496184e-01,\n",
+    "       -8.47740317e-01,  2.53055261e-01,  4.07728654e-01,  3.71441178e-01,\n",
+    "        2.60954268e-01,  6.91881170e-01,  1.35204560e+00,  1.66126779e+00,\n",
+    "        3.87633110e-02,  5.74308554e-02, -4.90039426e-01, -3.51960048e-01,\n",
+    "       -3.40826500e-01, -4.29096877e-01, -1.51329551e-01,  7.01787314e-01,\n",
+    "       -6.57970553e-01,  6.63866169e-01,  3.83965676e-01,  9.80863226e-01,\n",
+    "        1.26264195e+00,  1.60111452e+00,  1.71967437e+00,  1.27973984e+00,\n",
+    "       -1.32558645e-01,  1.04491727e+00,  1.02027173e+00,  1.30056175e+00,\n",
+    "        1.24801760e+00,  1.83402451e+00,  1.53001108e+00,  1.71737846e+00,\n",
+    "        1.57527834e+00,  2.04640540e+00,  2.42281956e+00,  8.27044471e-01,\n",
+    "        2.10345979e+00,  1.06201511e+00,  1.47960807e+00,  9.71628403e-01,\n",
+    "        5.21057069e-01,  5.03029167e-01,  4.21029664e-01,  1.10529512e+00,\n",
+    "        6.54941435e-01, -4.79959006e-01,  8.01560793e-01,  3.78517418e-01,\n",
+    "        5.08672026e-01,  1.68601802e-01, -3.90663606e-02,  1.62149508e+00,\n",
+    "        1.45669361e+00,  1.24135471e+00, -7.00934966e-01, -9.13895360e-01,\n",
+    "        4.83077266e-01,  4.50055520e-01, -1.17878557e+00, -6.26810617e-01,\n",
+    "        7.11486395e-02, -1.66610081e-01, -4.00110856e-01, -1.59543090e-01,\n",
+    "        3.69607167e-01,  1.05543185e+00,  1.27906214e+00,  1.23240685e+00,\n",
+    "        2.09226813e+00,  8.90031863e-01,  2.22894835e+00,  6.97854347e-01,\n",
+    "        1.09524514e+00,  3.24695184e-01,  7.28042788e-04, -4.45720862e-01,\n",
+    "       -5.73334022e-02,  1.23575766e+00,  1.31635328e-01,  8.67518743e-01,\n",
+    "        1.56059963e+00,  6.31070006e-01,  1.04464919e+00, -8.84947785e-01,\n",
+    "        1.31704400e+00,  9.86125573e-01,  2.02759151e+00,  1.48625969e+00,\n",
+    "        3.72770752e-02,  1.67338281e+00,  1.82601616e+00,  4.32808593e-01,\n",
+    "        1.91126665e+00, -1.40124317e-02, -6.15394675e-01,  1.24704749e+00,\n",
+    "        1.30533712e+00,  1.15308051e+00,  1.59741846e+00,  1.07918179e+00,\n",
+    "        3.55408514e-01,  1.15370940e-01, -8.76059920e-01, -1.33802778e+00,\n",
+    "        1.85639559e-01, -2.42649844e-01, -1.02996400e+00,  4.39484660e-01,\n",
+    "       -1.07934516e+00, -2.43198782e-01,  7.12058866e-01, -3.74227516e-01,\n",
+    "        8.88699033e-01, -3.97313023e-01,  2.86881825e-01,  4.79528683e-01,\n",
+    "        1.96059535e+00,  1.90794329e+00,  1.21424753e+00, -9.31731368e-03,\n",
+    "       -3.57476964e-01, -6.54898612e-01, -8.55912921e-01,  4.82253263e-01,\n",
+    "        7.82414953e-02, -1.16817948e-02,  1.02454754e-01,  4.75701199e-01,\n",
+    "        2.87974276e-02,  7.23507213e-01,  1.11294268e+00,  1.54304889e+00,\n",
+    "        2.55006078e+00,  1.97330796e+00,  4.55008481e-01,  9.08155981e-01,\n",
+    "       -3.16408466e-01, -9.11070592e-01, -1.19885254e+00, -1.49425998e+00,\n",
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+    "        9.09245114e-02, -8.33510548e-01, -7.73376882e-01,  4.20514784e-01,\n",
+    "        4.00816394e-01,  4.23429604e-01,  7.03190294e-01,  2.45607983e-01,\n",
+    "        1.12766363e-01,  2.29990495e-01,  1.44303637e-01,  2.98763762e-01,\n",
+    "        1.66542808e+00,  2.09103268e+00,  1.90449043e+00,  1.72099303e+00,\n",
+    "        1.25933300e+00,  2.29352112e+00,  1.97652899e-01,  1.17981554e+00,\n",
+    "       -1.11019238e+00, -3.31291763e-01, -4.81943208e-01, -1.57380538e+00,\n",
+    "       -8.01543386e-01, -1.57373293e+00, -6.11527712e-01, -1.17852491e+00,\n",
+    "        4.34070610e-01, -9.47518014e-01, -1.21053098e+00, -8.52856246e-01,\n",
+    "       -1.25192446e+00, -1.30987448e-01,  2.71523161e-01, -1.02593613e+00,\n",
+    "        4.19178470e-01, -3.32333953e-01, -7.02741725e-01,  1.18590220e+00])\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -36,8 +292,6 @@
     }
    ],
    "source": [
-    "#load data\n",
-    "data = np.loadtxt('generated_time_series.csv', delimiter=',', skiprows=1)\n",
     "\n",
     "#plot data\n",
     "plt.figure(figsize=(12, 4))\n",
@@ -148,8 +402,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "AR(1) vs AR(2) test statistic: -2 Critical value: 3.841458820694124\n",
-      "Fail to reject AR(1)\n",
+      "AR(1) vs AR(2) test statistic: 65.46386398456401 Critical value: 3.841458820694124\n",
+      "Reject AR(1) in favor of AR(2)\n",
       "AR(2) vs AR(3) test statistic: 3.5318291727430835 Critical value: 3.841458820694124\n",
       "Fail to reject AR(2)\n"
      ]
@@ -186,7 +440,6 @@
     "dof = 1\n",
     "crit = chi2.ppf(0.95, dof)\n",
     "test_stat = n * np.log(rss1 / rss2)\n",
-    "test_stat = -2 \n",
     "print('AR(1) vs AR(2) test statistic:', test_stat, 'Critical value:', crit)\n",
     "\n",
     "if test_stat > crit:\n",
@@ -215,7 +468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
diff --git a/book/time_series/arma.md b/book/time_series/arma.md
deleted file mode 100644
index 51133d3d..00000000
--- a/book/time_series/arma.md
+++ /dev/null
@@ -1,362 +0,0 @@
-(ARMA)=
-# ARMA process
-
-The main goal is to introduce the AutoRegressive Moving Average (ARMA) model to describe a **stationary stochastic process**. Hence the ARMA model can be applied on time series where e.g. trend and seasonality are not present / removed, and only noise remains, or after applying other methods [to obtain a stationary time series](stationarize).
-
-## Process definition
-
-In an ARMA model, we forecast the variable of interest using a linear combination of its past values plus the current and past errors. A zero mean ARMA process of orders $p$ and $q$ can be written as follows:
-
-$$S_t = \overbrace{\beta_1S_{t-1}+...+\beta_pS_{t-p}}^{\text{AR process}} + e_t + \overbrace{\theta_1 e_{t-1}+...+\theta_q e_{t-q}}^{\text{MA process}}$$ 
-
-or as
-
-$$S_t = \sum_{i=1}^p \beta_iS_{t-i}+e_t+\sum_{i=1}^q \theta_i e_{t-i}$$
-
-Each observation is made up of a **random error** $e_t$ at that epoch, a linear combination of **past observations**, and a linear combination of **past errors**. The errors $e_t$  are uncorrelated purely random noise process, known also as white noise. We note the process should still be stationary, satisfying
-
-$$\mathbb{E}(S_t)=0, \hspace{20px} \mathbb{D}(S_t)=\sigma^2,\quad \forall t$$
-
-This indicates that parts of the total variability of the process come from the signal and noise of past epochs, and only a (small) portion belongs to the noise of that epoch (denoted as $e_t$). To have a better understanding of the process itself, we consider two special cases, $q=0$ and $p=0$.
-
-### Special case 1: ARMA$(p,0) = $ AR$(p)$
-
-The first special case we are going to study considers $q=0$. A zero mean $p$-order autoregressive (AR) random process, abbreviated to ARMA($p,0$) = AR($p$), can be written as follows
-
-$$S_t = \beta_1S_{t-1}+...+\beta_pS_{t-p} + e_t=S_t = \sum_{i=1}^p \beta_iS_{t-i}+e_t$$
-
-#### First-order AR(1) process
-
-We will just focus on explaining $p=1$, i.e. the AR(1) process. A **zero-mean first order autoregressive** process can be written as follows
-
-$$S_t = \beta S_{t-1}+e_t, \hspace{20px} -1\leq\beta<1, \hspace{20px} t=2,...,m$$
-
-where $e_t$ is an i.i.d. noise process, e.g. distributed as $e_t\sim N(0,\sigma_{e}^2)$. See later the definition of $\sigma_{e}^2$.
-
-:::{card} Exercise
-
-In a zero-mean first order autoregressive process, abbreviated as AR(1), we have $m=3$ observations, $\beta=0.8$, and the generated white noise errors are $e = [e_1,\, e_2,\, e_3]^T=[1,\, 2,\, -1]^T$. What is the generated AR(1) process $S = [S_1,\, S_2,\, S_3]^T$?
-
-a. $S = \begin{bmatrix}1 & 2.8 & 1.24\end{bmatrix}^T$  
-b. $S = \begin{bmatrix} 0 & 2 & 0.6 \end{bmatrix}^T$  
-c. $S = \begin{bmatrix} 1 & 2 & -1 \end{bmatrix}^T$  
-
-```{admonition} Solution
-:class: tip, dropdown
-
-The correct answer is **a**. The AR(1) process can be initialized as $S_1=e_1=1$. The next values can be obtained through:
-
-$$
-S_t = \beta S_{t-1} + e_t
-$$
-
-Giving $S_2=0.8 S_1 + e_2 = 0.8\cdot 1 + 2 = 2.8$ and $S_3=0.8 S_2 + e_3 = 0.8\cdot 2.8 - 1= 1.24$, so we have:
-
-$$
-S = 
-\begin{bmatrix}1 & 2.8 & 1.24\end{bmatrix}^T 
-$$
-
-```
-:::
-
-**Formulation**
-
-Initializing $S_1=e_1$, with $\mathbb{E}(S_1)=\mathbb{E}(e_1)=0$ and $\mathbb{D}(S_1)=\mathbb{D}(e_1)=\sigma^2$. Following this, multiple applications of the above "autoregressive" formula ($S_t = \beta S_{t-1} + e_t$) gives:
-
-$$
-\begin{align*}
-S_1&=e_1\\ 
-S_2&=\beta S_1+e_2\\ 
-S_3 &= \beta S_2+e_3 = \beta^2S_1+\beta e_2+e_3\\ 
-&\vdots\\ 
-S_m &= \beta S_{m-1} + e_m = \beta^{m-1}S_1+\beta^{m-2}e_2+...+\beta e_{m-1}+e_m
-\end{align*}
-$$
-
-of which we still have (in order to impose the *stationarity*):
-
-$$\mathbb{E}(S_t)=0 \hspace{5px}\text{and}\hspace{5px} \mathbb{D}(S_t)=\sigma^2, \hspace{10px} t=1,...,m$$
-
-All the error components, $e_t$, are uncorrelated such that $Cov(e_t,e_{t+\tau})=0$ if $\tau \neq 0$, and with variance $\sigma_{e}^2$ which still needs to be determined.
-
-**Autocovariance**
-
-The mean of the process is zero and, therefore:
-
-$$\mathbb{E}(S_t) = \mathbb{E}(\beta S_{t-1}+e_t) = \beta\mathbb{E}(S_{t-1})+\mathbb{E}(e_t) = 0$$
-
-The variance of the process should remain constant as:
-
-$$\mathbb{D}(S_t) = \mathbb{D}(\beta S_{t-1} +e_t) \Leftrightarrow \sigma^2=\beta^2\sigma^2+\sigma_{e}^2, \hspace{10px} t\geq 2$$
-
-resulting in
-
-$$\sigma_{e}^2 = \sigma^2 (1-\beta^2)$$
-
-indicating that $\sigma_{e}^2$ is smaller than $\sigma^2$.
-
-The autocovariance (covariance between $S_t$ and $S_{t+\tau}$) is
-
-$$
-\begin{align*}
-c_{\tau}&=\mathbb{E}(S_t S_{t+\tau})-\mu^2 =\mathbb{E}(S_t S_{t+\tau})\\
-&= \mathbb{E}(S_t(\beta^\tau S_t + \beta^{\tau-1} e_{t+1}+...)) = \beta^\tau\mathbb{E}(S_t^2)=\sigma^2\beta^\tau
-\end{align*}$$
-
-In the derivation above we used that:
-
-$$
-\begin{align*}
-S_{t+\tau}=\beta^\tau S_t + \beta^{\tau-1} e_{t+1}+...+e_{t+\tau}
-\end{align*}
-$$
-
-and the fact that $S_t$ and $e_{t+\tau}$ are uncorrelated for $\tau \neq 0$.
-
-```{admonition} Derivation (optional)
-:class: tip, dropdown
-
-$$
-\begin{align*}
-S_{t+\tau}&= \beta^{t+\tau-1}S_1 + \beta^{t+\tau-2}e_2+...+ \beta^{\tau} e_{t}+ \beta^{\tau-1} e_{t+1}+...+e_{t+\tau}\\
-&= \beta^{\tau} \left(\beta^{t-1}S_1 + \beta^{t-2}e_2+...+  e_{t}\right)+ \beta^{\tau-1} e_{t+1}+...+e_{t+\tau}\\
-&=\beta^\tau S_t + \beta^{\tau-1} e_{t+1}+...+e_{t+\tau}
-\end{align*}
-$$
-
-```
-
-**Model structure of AR(1)**
-
-$$\mathbb{E}(S) = \mathbb{E}\begin{bmatrix}S_1\\ S_2\\ \vdots\\ S_m\end{bmatrix} = \begin{bmatrix}0\\ 0\\ \vdots\\ 0\end{bmatrix}, \hspace{15px} \mathbb{D}(S)=\Sigma_{S}=\sigma^2 \begin{bmatrix}1&\beta&...&\beta^{m-1}\\ \beta&1&...&\beta^{m-2}\\ \vdots&\vdots&\ddots&\vdots\\ \beta^{m-1}&\beta^{m-2}&...&1\end{bmatrix}$$
-
-* Autocovariance function $\implies$ $c_{\tau}=\sigma^2\beta^\tau$
-* Normalized autocovariance function (ACF) $\implies$ $\rho_\tau=c_{\tau}/c_0=\beta^\tau$
-* Larger value of $\beta$ indicates a long-memory random process
-* If $\beta=0$, this is called *purely random process* (white noise)
-* ACF is even, $c_{\tau}=c_{-\tau}=c_{|\tau|}$ and so is $\rho_{\tau}=\rho_{-\tau}=\rho_{|\tau|}$
-
-Later in this section we will see how the coefficient $\beta$ can be estimated.
-
-**Simulated example**
-
-A time series has been simulated to have a standard normal distribution, $S_i \sim N(0,1)$. This indicates that the first entry is $S_1 \sim \text{N}(0,1)$ and the remaining errors are $e_i \sim N(0,1-\beta^2)$, $i=2,...,m=1000$. The time series is shown in {numref}`ar1example`. Time correlation can be visually seen in the data.
-
-The normalized ACF shows the temporal correlation, $\rho_{\tau}=\beta^{\tau}$, where $\tau=0,1,2,...,m-1$.
-
-```{figure} ./figs/ar1example.png
-:name: ar1example
-:width: 600px
-:align: center
-
-Left: time series for $\beta =0.7$ and $\beta =-0.7$. Right: corresponding normalized autocovariance functions.
-```
-
-### Special case 2: ARMA$(0,q) = $ MA$(q)$
-
-A zero mean $q$-order moving average random process, abbreviated to ARMA(0,q) = MA(q), can be written as follows
-
-$$S_t=\theta_1 e_{t-1}+...+\theta_q e_{t-q}+e_t$$
-
-or
-
-$$S_t=\sum_{i=1}^q \theta_i e_{t-i} + e_t$$
-
-#### First-order MA(1) process
-
-Here we will just focus on the case $q=1$, i.e. MA(1). A **zero-mean first order moving average process** can be written as:
-
-$$S_t = \theta e_{t-1} + e_t, \hspace{10px} -1\leq\theta<1 \hspace{10px} t=2,...,m$$
-
-where $e_t$ is an i.i.d. noise process (white noise), e.g. distributed as $e_t\sim N(0,\sigma_{e}^2)$
-
-**Formulation**
-
-Initializing $S_1=e_1$, with $\mathbb{E}(S_1)=\mathbb{E}(e_1)=0$, $Var(S_1)=\sigma^2$ and $Var(e_i)=\sigma_{e}^2$ for $i=2,\dots,m$. Following this, multiple applications of the above "moving average" formula  gives:
-
-$$\begin{align*}S_1&=e_1\\ S_2&=\theta e_1+e_2\\ S_3 &= \theta e_2+e_3\\ &\vdots\\ S_m &= \theta e_{m-1} + e_m\end{align*}$$
-
-of which we still have (in order to impose the *stationarity*):
-
-$$\mathbb{E}(S_t)=0 \hspace{5px}\text{and}\hspace{5px} \mathbb{D}(S_t)=\sigma^2, \hspace{10px} t=1,...,m$$
-
-All the error components, $e_t$, are uncorrelated such that $Cov(e_t,e_{t+\tau})=0$ if $\tau\neq 0$, and the variance is $\sigma_e^2$.
-
-**Autocovariance**
-
-The mean of the process is zero and, therefore:
-
-$$\mathbb{E}(S_t) = \mathbb{E}(\theta e_{t-1}+e_t) =\theta\mathbb{E}(e_{t-1})+\mathbb{E}(e_t) = 0$$
-
-The variance of the process should remain constant as:
-
-$$\mathbb{D}(S_t) = \mathbb{D}(\theta e_{t-1}+e_t) \Leftrightarrow  \sigma^2=\theta^2\sigma_e^2+\sigma_e^2, \hspace{10px} t\geq 2$$
-
-resulting in
-
-$$ \sigma_e^2 = \frac{\sigma^2}{1+\theta^2}$$
-
-indicaating that $\sigma_e^2$ is smaller than $\sigma^2$
-
-The autocovariance is
-
-$$c_1=Cov(S_t, S_{t+1}) = \sigma_e^2\theta\\ c_{-1}=Cov(S_t, S_{t-1}) =  \sigma_e^2\theta$$
-
-and
-
-$$c_{\tau}=Cov(S_t,S_{t+\tau}) = 0, \hspace{10px}\text{for}\hspace{5px}\tau\geq 2$$
-
-The normalized auto-covariance function (ACF) follows:
-
-$$\rho_{\tau}=\frac{c_{\tau}}{\sigma^2}=\begin{cases}\frac{\theta}{1+\theta^2}, \hspace{5px}&\text{if}\hspace{5px}\tau=1\\ 0, \hspace{5px}&\text{if}\hspace{5px}\tau\neq 1\end{cases}
-$$
-
-**Model structure**
-
-$$\mathbb{E}(S) = \mathbb{E}\begin{bmatrix}S_1\\ S_2\\ \vdots\\ S_m\end{bmatrix} = \begin{bmatrix}0\\ 0\\ \vdots\\ 0\end{bmatrix}, \hspace{15px} \mathbb{D}(S)=\Sigma_{S}=\sigma^2\begin{bmatrix}1&\rho_1&0&\dots&0\\ \rho_1&1&\rho_1& &\\ 0&\rho_1&1&\ddots&0\\ \vdots& &\ddots&\ddots&\rho_1\\ 0&\dots&0&\rho_1&1\end{bmatrix}$$
-
-In summmary: 
-
-* Autocovariance function $\implies$ $c_{\tau}=\begin{cases}\frac{\sigma^2\theta}{1+\theta^2}, \hspace{5px}&\text{if}\hspace{5px}\tau=1\\ 0, \hspace{5px}&\text{if}\hspace{5px}\tau>1\end{cases}$
-
-* Normalized auto-covariance function (ACF) $\implies$ $\rho_\tau=\begin{cases}\frac{\theta}{1+\theta^2}, \hspace{5px}&\text{if}\hspace{5px}\tau=1\\ 0, \hspace{5px}&\text{if}\hspace{5px}\tau\neq 1\end{cases}$
-
-* ACF is even, $c_{\tau}=c_{-\tau}=c_{|\tau|}$ and so is $\rho_{\tau}=\rho_{-\tau}=\rho_{|\tau|}$
-
-**Simulated example**
-
-A time series has been simulated to have a standard normal distribution, $e_i \sim \text{N}(0,1)$. This indicates that the entries of $S$ have $S_i \sim \text{N}(0,1+\theta^2)$, $i=1,...,m=1000$, where the variance of the noise process is $\sigma^2 = 1+\theta^2$. In fact, $\sigma_{e_t}=1$, but not the random process MA(1) in total. The time series is shown in {numref}`ma1ex`. 
-
-The normalized ACF shows the temporal correlation, $\rho_{\tau}=\frac{\theta}{1+\theta^2}$, if $\tau=1$, and $\rho_{\tau}=0$ if $\tau>1$.
-
-MMMMM should delete the equation in the right panels!
-
-```{figure} ./figs/ma1ex.png
-:name: ma1ex
-:width: 600px
-:align: center
-
-Left: time series for $\beta =0.9$ and $\beta =-0.9$. Right: corresponding normalized autocovariance functions.
-```
-
-## Estimation of coefficients of ARMA process
-
-If the values of $p$ and $q$ of the ARMA($p,q$) process are known, the question is: **how can we estimate the coefficients $\beta_1,...,\beta_p$ and $\theta_1,...,\theta_q$?**
-
-Here, we only elaborate on AR(2) = ARMA(2,0) using best linear unbiased estimation (BLUE) to estimate $\beta_1$ and $\beta_2$. The method can be generalized to estimate the parameters of an ARMA($p,q$) process.
-
-**Example: Parameter estimation of AR(2)**
-
-The AR(2) process is of the form
-
-$$S_t=\beta_1 S_{t-1}+\beta_2 S_{t-2}+e_t$$
-
-In order to esitimate the $\beta_i$ we can set up the following linear model of observation equations (starting from $t=3$):
-
-$$\begin{bmatrix}S_3 \\ S_4 \\ \vdots \\ S_m \end{bmatrix} = \begin{bmatrix}S_2 & S_1 \\S_3 & S_2\\ \vdots & \vdots\\ S_{m-1}&S_{m-2} \end{bmatrix}\begin{bmatrix}\beta_1 \\ \beta_2\end{bmatrix} + \begin{bmatrix}e_{3} \\ e_{4}\\ \vdots \\ e_{m} \end{bmatrix}$$
-
-The BLUE estimator of $\beta=[\beta_1,\beta_2]^T$ is
-
-$$\hat{\beta}=(\mathrm{A}^T\mathrm{A})^{-1}\mathrm{A}^TS$$
-
-
-## Worked example - Single Differencing
-
-On this worked example, we will show that [single differencing](SD) induces an MA(1) process. The original time series is given as:
-
-$$Y=\begin{bmatrix}Y_1\\ Y_2\\ \vdots \\ Y_m\end{bmatrix}, \hspace{10px} \Sigma_{Y}=\sigma^2 I_m$$
-
-We apply single differencing which in this case results in a purely random process:
-
-$$\begin{cases}S_1 = \Delta Y_1 = Y_1\\ S_2=\Delta Y_2 = Y_2 - Y_1\\ S_3=\Delta Y_3 = Y_3-Y_2\\ \quad\vdots \\ S_m= \Delta Y_m = Y_m - Y_{m-1}\end{cases}$$
-
-In matrix notation, this can be written as:
-
-$$\begin{bmatrix} S_1\\  S_2\\ \vdots \\  S_m\end{bmatrix} = \underbrace{\begin{bmatrix}
-    1 & 0 &   & \dots & 0\\
-    -1 & 1 & 0 &   &  \\
-    0 & -1 & 1 & \ddots & \\
-    \vdots & \ddots &\ddots & \ddots & 0 \\
-    0 & \dots & 0 & -1 & 1
-\end{bmatrix}}_{\mathrm{T}}\begin{bmatrix}Y_1\\ Y_2\\ \vdots \\ Y_m\end{bmatrix} \Longleftrightarrow S = \mathrm{T}Y$$
-
-We apply the [variance propagation law](01_LinearProp):
-
-$$\Sigma_{ S}=\mathrm{T}\Sigma_{Y}\mathrm{T}^T = \mathrm{T}\sigma^2I_m\mathrm{T}^T=\sigma^2\mathrm{TT}^T$$
-
-such that we obtain:
-
-$$\Sigma_{S} = \sigma^2\mathrm{TT}^T = 2\sigma^2\begin{bmatrix}1&-0.5&0&\dots&0\\ -0.5&1&-0.5& &\\ 0&-0.5&1&\ddots&0\\ \vdots& &\ddots&\ddots&-0.5\\ 0&\dots&0&-0.5&1\end{bmatrix}$$
-
-We can see that the structure indeed corresponds with the covariance matrix of an AR(1) process, from which we see that $\rho_1=-0.5$. Now we can find the value of $\theta$: 
-
-$$\begin{cases}\rho_1=-0.5=\frac{\theta}{1+\theta^2}\\  S_t = \theta e_{t-1}+e_t\end{cases}\implies \theta=-1 \implies S_t = e_t-e_{t-1}$$
-
-:::{card} Exercise
-
-For the stationary AR(2) process, calculate the ACF at lag 1. In other words, calculate $\rho_1$.
-
-```{admonition} Solution
-:class: tip, dropdown
-
-For the AR($p$) process we know that $\mathbb{E}(S_t)=0$, and $Var(S_t)=\sigma^2$ ($\forall t$), and
-
-$$S_t = \beta_1S_{t-1}+\beta_2S_{t-2}+e_t=
-\begin{bmatrix}\beta_1 & \beta_2 & 1\end{bmatrix}\begin{bmatrix}S_{t-1} \\ S_{t-2} \\ e_t\end{bmatrix}$$
-
-To compute the autocovariance function at lag 1, $c_1$, we need to compute the covariance between $S_{t-1}$ and $S_t$, which is given as
-
-$$
-\begin{align*}
-c_1 &= \mathbb{E}(S_{t-1}S_t)
-= \mathbb{E}\left(S_{t-1}
-(\beta_1 S_{t-1} + \beta_2 S_{t-2} + e_t)
-\right)
-\\
-&= \beta_1 \mathbb{E}(S_{t-1}^2)
-+ \beta_2 \mathbb{E}(S_{t-2}S_{t-1})
-+ \mathbb{E}(S_{t-1}e_t)\\
-&= \beta_1 \sigma^2
-+ \beta_2 c_1
-\end{align*}$$
-
-
-which gives
-
-$$
-\beta_1 \sigma^2 = c_1(1-\beta_2)
-$$
-
-or, because $\rho_1=c_1/\sigma^2$:
-
-$$
-\rho_1=\frac{\beta_1}{1-\beta_2}
-$$
-
-```
-:::
-
-## Brief Summary
-
-The random processes (noise processes) considered here are:
-
-* ARMA($p,q$) process
-
-$$
-S_t = \sum_{i=1}^p \beta_iS_{t-i}+e_t+\sum_{i=1}^q\theta_ie_{t-1}
-$$
-
-* AR($p$) process
-
-$$
-S_t = \sum_{i=1}^p \beta_iS_{t-i}+e_t
-$$
-
-* MA($q$) process
-
-$$
-S_t = e_t+\sum_{i=1}^q\theta_ie_{t-1}
-$$
-
-The parameters of these stochastic processes can be estimated using the least-squares method. This allows then to predict the stochastic process, needed for [forecasting](forecast).
\ No newline at end of file
diff --git a/book/time_series/exercise2.ipynb b/book/time_series/exercise2.ipynb
deleted file mode 100644
index 8b3a7eb6..00000000
--- a/book/time_series/exercise2.ipynb
+++ /dev/null
@@ -1,251 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Stationary time series "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from statsmodels.tsa.stattools import adfuller \n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Introduction:** \n",
-    "\n",
-    "[In the previous exercise](https://mude.citg.tudelft.nl/book/time_series/exercise1.html#) we created and plotted the $Y_4$ time series, now we check its stationarity. Remember that we need to ensure *stationarity* of the time series data-set for *forecasting and predictive models*. \n",
-    "In this excercise, you can test the stationarity of the time series using transformation and visual inspection and the Augmented Dickey-Fuller (ADF) test (The ADF test is optional). \n",
-    "\n",
-    "**Background knowledge:** \n",
-    "\n",
-    "The ADF test can be performed by using two hypotheses (Null Hypothesis and Alternative Hypothesis):\n",
-    "\n",
-    "1. Null Hypothesis $H_o$: we assume that the time series is not stationary. \n",
-    "2. Althernative Hypothesis $H_a$: we assume that the time series is stationary. \n",
-    "\n",
-    "If the test statistic is smaller than the critical value, the null hypothesis is rejected and therefore the time series is stationary. In this case the the p-value becomes very small. In python, there is a package: **statsmodels** which has the function of **adfuller method**. We use the <code>adfuller()</code> function to test the stationarity of the data-set. Regarding the interpretation of the adfuller function, the first output is the test-statistic, the second one is the p-value, etc.\n",
-    "\n",
-    "**Excercise:** \n",
-    "\n",
-    "We take the time series and the noise from the Excercise 1 $Y_2$, $Y_4$ and $\\epsilon_t$. We also use the single differencing method to make the time series stationary and plot the results. Later we will also use the least squares method (best linear unbiased estimation - BLUE) to de-trend the data. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Note:**\n",
-    "\n",
-    "You don't need to focus on the next cell, it contains the code included in the [Time series components exercise](https://mude.citg.tudelft.nl/book/time_series/exercise1.html#) for creating the time series."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.random.seed(0)  # For reproducibility\n",
-    "\n",
-    "# create observations\n",
-    "time = np.arange(501) \n",
-    "m = len(time)\n",
-    "y_0 = 1 \n",
-    "r = 0.02 \n",
-    "y1 = y_0 + r*time \n",
-    "\n",
-    "# introduce a seasonality\n",
-    "omega = 2 * np.pi/100 \n",
-    "A = 1 \n",
-    "phi_0 = 0.2*np.pi\n",
-    "y2 = y1 + A*np.sin(omega * time + phi_0) \n",
-    "\n",
-    "# introduce offset\n",
-    "t_k = 300 \n",
-    "O_k = 5 \n",
-    "y3 = y2.copy() \n",
-    "y3[t_k:] = y3[t_k:] + O_k\n",
-    "\n",
-    "# introduce random error\n",
-    "mean = 0 \n",
-    "sigma = 0.5 \n",
-    "et = np.random.normal(loc = mean, scale = sigma, size = m) \n",
-    "y4 = y3 + et "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We start applying single differencing to check whether the time series becomes stationary. We do it first for <code>y2</code>, which just contains the observations and the seasonality."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Single Differencing')"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "diff_y2 = np.diff(y2)\n",
-    "diff_y2 = np.insert(diff_y2, 0, 0)\n",
-    "\n",
-    "plt.figure(figsize=(8,4))\n",
-    "plt.grid()\n",
-    "plt.plot(time, diff_y2)\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('Time')\n",
-    "plt.title('Single Differencing')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<code>y2</code> clearly looks non-stationary. Can you explain why?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We repeat the above procedure for <code>y4</code>, which also includes offset and noise."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Single Differencing')"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "diff_y4 = np.diff(y4)\n",
-    "diff_y4 = np.insert(diff_y4, 0, 0)\n",
-    "\n",
-    "plt.figure(figsize=(8,4))\n",
-    "plt.grid()\n",
-    "plt.plot(time, diff_y4)\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('Time')\n",
-    "plt.title('Single Differencing')\n",
-    "# it looks stationary, but we show it using ADF test"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<code>y4</code> now seems to be stationary, but to prove it we need to do the ADF test, which is optional material."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "ADF test is used to show that the single has produced a stationary dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test statistics:-15.24, pvalue:0.0000, Critical_value(1%):-3.44\n"
-     ]
-    }
-   ],
-   "source": [
-    "test_diff_y4 = adfuller(diff_y4)\n",
-    "test_statistic = test_diff_y4[0]\n",
-    "p_value = test_diff_y4[1]\n",
-    "critical_value = test_diff_y4[4]\n",
-    "print(f'Test statistics:{test_statistic:.2f}, pvalue:{p_value:.4f}, Critical_value(1%):{critical_value[\"1%\"]:.2f}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Since the test statistic is smaller than the critical value and the p-value is small, the Null hypothesis $H_0$ is rejected. This means that the time series is stationary!"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "mude2",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/book/time_series/exercise3.ipynb b/book/time_series/exercise3.ipynb
deleted file mode 100644
index 86820769..00000000
--- a/book/time_series/exercise3.ipynb
+++ /dev/null
@@ -1,225 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Time series modelling "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from statsmodels.graphics.tsaplots import plot_acf\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Introduction:** \n",
-    "\n",
-    "In this exercise, you will focus on the Best Linear Unbiased Estimation (BLUE). With BLUE, if the components of the time series are known, you can use the linear model of observations to estimate these components. \n",
-    "\n",
-    "**Exercise:** \n",
-    "\n",
-    "In this excercise, you calculate the BLUE estimates. First, create your matrix $A$ and $\\Sigma_{Y}$ which need to have dimensions of 501x5 (501: rows and 5 columns) and 501x501 respectively. Can you explain what these 5 parameters are? For $\\Sigma_{Y}$, you can use the np.eye function from numpy. Having defined these two matrices, we can obtain the BLUE estimats of \n",
-    "\n",
-    "$$\n",
-    "\\hat{X}=(A^T \\Sigma_{Y}^{-1}A)^{-1}A^T \\Sigma_{Y}^{-1}Y,\\, \\, \\hat{Y}=...,\\, \\, \\hat{\\epsilon}=...\n",
-    "$$ \n",
-    "\n",
-    "along with their covariance matrices $\\Sigma_{\\hat{X}}=(A^T \\Sigma_{Y}^{-1}A)^{-1}$, $\\Sigma_{\\hat{Y}}=...$ and $\\Sigma_{\\hat{\\epsilon}}=...$. \n",
-    "\n",
-    "After you have estimated the $\\hat{X}$ (having 5 elements), we you can compare each element of the $\\hat{x}$ with the corresponding values from the original time series you simulated ($y_0$, $r$, $A_m$, $\\phi$, $o_k$). The precision of the parameters can also be obtained from $\\Sigma_{\\hat{X}}$. You may also want to follow hypothesis tests to test the statistical significance of the estimated parameters. \n",
-    "\n",
-    "Please note that $A_m$ has been previously defined as $A$, but for avoiding confusion with the $A$ matrix has been renamed."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Note:**\n",
-    "\n",
-    "You don't need to focus on the next cell, it contains the code included in the [Time series components exercise](https://mude.citg.tudelft.nl/book/time_series/exercise1.html#) for creating the time series."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.random.seed(0)  # For reproducibility\n",
-    "\n",
-    "# create observations\n",
-    "time = np.arange(501) \n",
-    "m = len(time)\n",
-    "y_0 = 1 \n",
-    "r = 0.02 \n",
-    "y1 = y_0 + r*time \n",
-    "\n",
-    "# introduce a seasonality\n",
-    "omega = 2 * np.pi/100 \n",
-    "Am = 1 \n",
-    "phi_0 = 0.2*np.pi\n",
-    "y2 = y1 + Am*np.sin(omega * time + phi_0) \n",
-    "\n",
-    "# introduce offset\n",
-    "t_k = 300 \n",
-    "O_k = 5 \n",
-    "y3 = y2.copy() \n",
-    "y3[t_k:] = y3[t_k:] + O_k\n",
-    "\n",
-    "# introduce random error\n",
-    "mean = 0 \n",
-    "sigma = 0.5 \n",
-    "et = np.random.normal(loc = mean, scale = sigma, size = m) \n",
-    "y4 = y3 + et "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We first create the $A$ matrix which is based on linear regression and seasonality. We then include the offset in a new column and consequently create the covariance matrix $\\Sigma_Y$. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A = np.stack((np.ones(m), time, np.cos(omega*time), np.sin(omega*time)), axis=1)\n",
-    "u = np.zeros(m)\n",
-    "u[t_k:] = 1\n",
-    "A = np.column_stack((A,u))\n",
-    "Sigma_Y = (sigma**2) * np.eye(m) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now implement BLUE, meaning that we compute $\\hat X$, $\\hat Y$, \\hat \\psilon$ and the covariance matrix $\\Sigma_{\\hat{X}}$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Xhat = np.linalg.inv(A.T @ np.linalg.inv(Sigma_Y) @ A) @ A.T @ np.linalg.inv(Sigma_Y) @ y4\n",
-    "Yhat = A @ Xhat \n",
-    "ehat = y4 - Yhat \n",
-    "Sigma_xhat = np.linalg.inv(A.T @ np.linalg.inv(Sigma_Y) @ A)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We then compare the observed (true) values of $y_0, r, A_m, \\phi_0$ and $o_k$ with the predicted ones."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "y0: True value is: 1 ,  Estimated value is: 1.0108670424241462\n",
-      "r: True value is: 0.02 ,  Estimated value is: 0.020019382911012216\n",
-      "Am: True value is: 1 ,  Estimated value is: 1.0971057283981793\n",
-      "phi0: True value is: 0.6283185307179586 ,  Estimated value is: 0.6899387295464864\n",
-      "Ok: True value is: 5 ,  Estimated value is: 4.929702224035133\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Comparisons of xhat with the initial (true) values x:\n",
-    "y_0_hat = Xhat[0] # compare with y_0\n",
-    "print('y0: True value is:', y_0,',  Estimated value is:', y_0_hat)\n",
-    "r_hat = Xhat[1]   # compare with r\n",
-    "print('r: True value is:', r,',  Estimated value is:', r_hat)\n",
-    "\n",
-    "Am_hat = np.sqrt(Xhat[2]**2 + Xhat[3]**2) # compare with Am\n",
-    "print('Am: True value is:', Am,',  Estimated value is:', Am_hat)\n",
-    "\n",
-    "phi_0_hat = np.arctan(Xhat[2]/Xhat[3]) # compare with phi_0\n",
-    "print('phi0: True value is:', phi_0,',  Estimated value is:', phi_0_hat)\n",
-    "\n",
-    "O_k_hat = Xhat[4]  # compare with O_k\n",
-    "print('Ok: True value is:', O_k,',  Estimated value is:', O_k_hat)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now plot the observed and estimated time series. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 5))\n",
-    "plt.grid()\n",
-    "plt.plot(time, y4, label='Original $Y$ (with noise)', color='pink')\n",
-    "plt.plot(time, Yhat, label='Estimated $Y$: yhat', color='r')\n",
-    "plt.plot(time, y3, label='True $Y$ (without noise)', linestyle='--', color='b')\n",
-    "plt.title('Time series modelling')\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('time')\n",
-    "plt.legend();"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "TAMude",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/book/time_series/exercise4.ipynb b/book/time_series/exercise4.ipynb
deleted file mode 100644
index 4362fa96..00000000
--- a/book/time_series/exercise4.ipynb
+++ /dev/null
@@ -1,196 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Autocovariance function (ACF) and PSD "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from statsmodels.graphics.tsaplots import plot_acf\n",
-    "from scipy import signal\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Introduction:** \n",
-    "\n",
-    "In this exercise, you will focus on normalized auto-covariance function (ACF) and the power spectral density (PSD), and the auto-regressive moving average (ARMA).\n",
-    "\n",
-    "**Background knowledge:** \n",
-    "\n",
-    "The package <code>statsmodels.graphics.tsaplots</code> contains functions for computing ACF and PSD. <code>plot_acf</code> is one of these functions, which create automatically a plot. Regarding the PSD, there is a function from the package of <code>scipy.signal</code> and you need to use the <code>signal.periodogram</code> to calculate the PSD. An alternative way to compute the PSD is based on the least-squares harmonic estimation (LS-HE), which is based on hypothesis testing.\n",
-    "\n",
-    "**Exercise:** \n",
-    "\n",
-    "We use the above functions to plot the ACF and PSD of white noise time series. Later we also compute them for the ARMA(p,q) process. We generate a white noise process, similar to that created in [Time series components exercise](https://mude.citg.tudelft.nl/book/time_series/exercise1.html#) ($m=501$). We will see that white noise does not show any temporal correlation. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We start defining the parameters of the white noise $\\epsilon \\sim \\textbf{N} (\\mu=0, \\sigma_{\\epsilon}^2=1)$. As previously done in the [Time series components exercise](https://mude.citg.tudelft.nl/book/time_series/exercise1.html#), the number of observation is $m=501$ and the time interval is $\\Delta t = 1$ s. The sampling rate is chosen equal to $f_s=1$ Hz."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0, 'Time (s)')"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mean1 = 0 \n",
-    "sigma1 = 1\n",
-    "m = 501\n",
-    "time = np.arange(m) \n",
-    "Fs = 1 \n",
-    "\n",
-    "e = np.random.normal(loc = mean1, scale = sigma1, size = m) \n",
-    "yt = e\n",
-    "\n",
-    "# plot the time series\n",
-    "plt.figure()\n",
-    "plt.grid()\n",
-    "plt.plot(time, yt, color='pink')\n",
-    "plt.title(r'White noise $\\epsilon$ time series')\n",
-    "plt.ylabel(r'$Y$(t)')\n",
-    "plt.xlabel('Time (s)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now plot the normalized auto-covariance function (ACF) of the generated noise.\n",
-    "Look at the time series. Do you see temporal correlation?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ACF = plot_acf(yt, lags=None, alpha=0.05, title=r'ACF of white noise $\\epsilon$', color='pink')\n",
-    "plt.grid()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also plot the PSD of the white noise. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0, 'Frequency (Hz)')"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# plot the white noise PSD\n",
-    "F, PSD = signal.periodogram(yt, fs=Fs, scaling='density', return_onesided=False)\n",
-    "\n",
-    "plt.figure()\n",
-    "plt.grid()\n",
-    "plt.plot(F, PSD, color='pink')\n",
-    "plt.title(r'PSD of white noise $\\epsilon$')\n",
-    "plt.ylabel('Power PSD')\n",
-    "plt.xlabel('Frequency (Hz)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The PSD values seem not to be frequency dependent: it looks flat indicating that all frequencies have identical contributions, even though there are some peaks. Think of white light, that has similar characteristics."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "mude2",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/book/time_series/exercise5.ipynb b/book/time_series/exercise5.ipynb
deleted file mode 100644
index 32ae8c5d..00000000
--- a/book/time_series/exercise5.ipynb
+++ /dev/null
@@ -1,228 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ARMA: MA(1), ACF + PSD "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from statsmodels.graphics.tsaplots import plot_acf\n",
-    "from scipy import signal\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Introduction:** \n",
-    "\n",
-    "In this exercise, we will focus on special case of ARMA(p,q) process, namely ARMA(0,1)=MA(1) process. We then compare the ACF and PSD of the generated time series.\n",
-    "\n",
-    "**Exercise:** \n",
-    "\n",
-    "We generate a MA(1) time series. As you know from the lectures an MA(1) is of the form\n",
-    "\n",
-    "$$\n",
-    "Y_t = \\theta \\epsilon_{t-1}+ \\epsilon_{t}\n",
-    "$$\n",
-    "\n",
-    "We assume $\\theta=0.8$, and the time series is assumed to be stationary, so $\\mathbb{E}(Y_t)=0$ and $\\mathbb{D}(Y_t)=\\sigma^2$, with $\\sigma=1$. For generating the time series, we need an initialization of one sample generated randomly as $(0,\\sigma^2)$, and then use the above recursive formulae. The variance of $\\epsilon_t$ is obtained from\n",
-    "\n",
-    "$$\n",
-    "\\sigma_{\\epsilon}^2 = \\frac{\\sigma^2}{1+\\theta^2}\n",
-    "$$\n",
-    "\n",
-    "We can then apply the ACF and PSD to the generated MA(1) noise process."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We start defining the parameters of the white noise $\\epsilon \\sim \\textbf{N} (\\mu=0, \\sigma_{\\epsilon}^2=1)$. As previously done in the [Time series components exercise](https://mude.citg.tudelft.nl/book/time_series/exercise1.html#), the number of observation is $m=501$ and the time interval is $\\Delta t = 1$ s. The sampling rate is chosen equal to $f_s=1$ Hz.\n",
-    "\n",
-    "We also define $\\sigma_{\\epsilon}$ and create the arrays for $Y$ and $\\epsilon$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# simulate an ARMA(0,1)=MA(1) noise process (moving average of order 1)\n",
-    "mean2 = 0 \n",
-    "sigma2 = 1\n",
-    "theta = 0.8\n",
-    "m = 501\n",
-    "time = np.arange(m) \n",
-    "Fs = 1 \n",
-    "\n",
-    "sigma_e = np.sqrt(sigma2**2/(1+theta**2))\n",
-    "y = np.zeros(m) \n",
-    "e = np.zeros(m) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now initialize the first entry of <code>e</code> and <code>y</code> such that $Y(0)=\\epsilon(0)$ and then we can loop using the function $Y_t = \\theta \\epsilon_{t-1}+ \\epsilon_{t}$ defined above. Note that all entries of $\\epsilon$ are random numbers, but $\\epsilon (0)$ depends on $\\sigma^2$, while the others on $\\sigma_{\\epsilon}$.\n",
-    "\n",
-    "Then we plot the time series."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0, 'time')"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "e[0] = np.random.randn() * sigma2\n",
-    "y[0] = e[0]\n",
-    "\n",
-    "for i in range(1, m):\n",
-    "    e[i] = np.random.randn() * sigma_e\n",
-    "    y[i] = theta * e[i-1] + e[i]\n",
-    "\n",
-    "plt.figure()\n",
-    "plt.grid()\n",
-    "plt.plot(time, y, color='pink')\n",
-    "plt.title('MA(1) time series')\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('time')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now compute and plot the ACF of the MA(1) process."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ACF = plot_acf(y, lags=None, alpha=0.05, title='ACF of MA(1)', color='pink')\n",
-    "plt.grid()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Eventually we plot the PSD of the MA(1) process."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0, 'Frequency')"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "F, PSD = signal.periodogram(y, fs=Fs, scaling='density', return_onesided=False)\n",
-    "plt.figure()\n",
-    "plt.grid()\n",
-    "plt.plot(F, PSD, color='pink')\n",
-    "plt.title('PSD of MA(1)')\n",
-    "plt.ylabel('Power: PSD')\n",
-    "plt.xlabel('Frequency')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The PSD values seem to have larger values at lower frequencies. This indicates that lower frequencies have higher contribution to data variability (as the moving average reduces the high frequency noise). "
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "mude2",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/book/time_series/fit_BLUE.ipynb b/book/time_series/fit_BLUE.ipynb
index 4c855536..9b48cfd8 100644
--- a/book/time_series/fit_BLUE.ipynb
+++ b/book/time_series/fit_BLUE.ipynb
@@ -10,8 +10,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
+   "execution_count": 8,
+   "metadata": {
+    "tags": [
+     "thebe-remove-input-init",
+     "auto-execute-page"
+    ]
+   },
    "outputs": [],
    "source": [
     "# Import necessary libraries\n",
@@ -20,13 +25,112 @@
     "import matplotlib.pyplot as plt\n",
     "\n",
     "# Import the data\n",
-    "data = np.loadtxt(\"y_values.csv\", delimiter=\",\")\n",
+    "data = np.array([11.45597157, 15.94789764, 12.30148271,  9.13358141,  2.58184562,\n",
+    "       10.95456964,  3.69809318, 11.44636424, 10.80544783, 12.38098492,\n",
+    "       11.54897375, 19.31194611, 12.12252661,  9.98599556, 11.60389914,\n",
+    "       10.46209933,  8.96147123,  7.11245384,  4.26347084, 10.7519177 ,\n",
+    "        7.81589454, 16.66295892, 18.48344424, 17.5974714 , 13.2228174 ,\n",
+    "       16.31125138, 12.33660561,  2.33154242,  2.93452744,  9.13964713,\n",
+    "        8.36681806,  8.98550456, 13.99953011, 14.12102914, 19.83967076,\n",
+    "       18.05770602, 16.81519874, 10.84129196,  6.11617555, 10.70419501,\n",
+    "        9.99589668,  6.14546554, 11.77688896, 11.10459271, 14.3101019 ,\n",
+    "       18.02580669, 25.48864304, 21.63665269, 14.34135119, 16.76686106,\n",
+    "       13.47714406, 11.94351168, 10.78873477, 10.59644265,  6.60760718,\n",
+    "       12.68881244, 18.96558167, 21.35281324, 22.04885378, 21.05606879,\n",
+    "       12.58249621, 15.33167125, 17.28303595,  4.09987902, 13.08452483,\n",
+    "       19.53672184, 14.65426035, 18.03419113, 22.53381169, 21.38477433,\n",
+    "       25.03150635, 14.36365534, 17.91128252, 23.20698379, 20.33601078,\n",
+    "       17.13460094, 15.92905078, 11.47332787, 15.84490286, 22.24778677,\n",
+    "       22.42234145, 25.46102446, 27.27332887, 22.72454444, 19.48988589,\n",
+    "       17.22496605, 16.82470942, 15.6172585 , 12.88341355, 10.06804755,\n",
+    "       19.27404251, 20.06063283, 21.26023394, 23.63604259, 23.31337453,\n",
+    "       16.49804107, 23.65583924, 20.63226704, 17.81420825, 20.33008935,\n",
+    "       12.32074957, 18.06214985, 22.12335576, 22.14170638, 25.35997315,\n",
+    "       24.16451389, 27.45449635, 22.1276958 , 21.1225919 , 25.75348599,\n",
+    "       21.3301569 , 16.00975066, 14.13981402, 16.80413373, 26.24239636,\n",
+    "       24.28698747, 28.1689711 , 25.07763479, 24.85185391, 25.73729435,\n",
+    "       25.46118482, 26.69798825, 15.52087717, 14.28426396, 14.6935964 ,\n",
+    "       24.01857145, 25.89839835, 20.87540699, 27.11000048, 27.70532329,\n",
+    "       29.75270489, 22.95154796, 17.93809626, 17.12560764, 25.4752212 ,\n",
+    "       21.2062299 , 20.62191849, 22.02207213, 23.14056749, 27.28265646,\n",
+    "       30.03630198, 30.87332825, 29.68505463, 23.18109553, 23.10760677,\n",
+    "       24.6859994 , 17.73793252, 21.23716515, 22.55773239, 24.68434915,\n",
+    "       30.5539557 , 28.20042491, 29.86457394, 29.64903893, 29.87558983,\n",
+    "       25.44145766, 25.15128924, 21.75098338, 22.4843817 , 19.33684734,\n",
+    "       23.80925989, 24.9061194 , 24.61038011, 30.64163941, 28.31755917,\n",
+    "       36.32319151, 27.88528602, 36.65099753, 24.35778143, 31.66056825,\n",
+    "       21.55999167, 24.0877411 , 23.91592159, 27.17407354, 22.38060053,\n",
+    "       30.05883444, 29.13173921, 32.48554106, 29.90306969, 23.73822464,\n",
+    "       26.77315697, 25.67583368, 24.59395502, 23.01646767, 30.7258133 ,\n",
+    "       28.4909835 , 26.33665084, 32.10337929, 32.25584843, 37.21282467,\n",
+    "       32.84297716, 27.19689338, 26.78998349, 25.67384565, 20.14723868,\n",
+    "       27.35869997, 24.26742206, 30.8019133 , 31.66529389, 32.97336876,\n",
+    "       33.8577623 , 36.78463913, 29.45295741, 25.85279108, 26.60454342,\n",
+    "       24.71411438, 19.93564663, 25.71481838, 25.79991399, 31.41324447,\n",
+    "       36.83235238, 32.60684507, 37.18868723, 38.96180298, 32.54094749,\n",
+    "       25.88625055, 29.15889181, 31.86416904, 30.40862388, 33.57578034,\n",
+    "       29.13713622, 29.35594449, 36.24120669, 38.10897434, 37.94122838,\n",
+    "       35.84574435, 32.21622382, 33.9304986 , 29.18851557, 28.42731942,\n",
+    "       25.00423572, 35.09645178, 32.84900925, 31.32617723, 32.81378915,\n",
+    "       37.29071177, 37.53783153, 43.29440974, 35.59607042, 27.53980752,\n",
+    "       30.49041655, 31.19793188, 30.4689153 , 30.03845169, 35.01211804,\n",
+    "       30.85010531, 39.50696476, 43.72093536, 46.44965595, 35.85033201,\n",
+    "       39.13345467, 38.53149985, 26.19354375, 26.44781018, 26.80799059,\n",
+    "       30.97722805, 36.64374683, 34.37789527, 41.45921651, 39.04985636,\n",
+    "       37.06862959, 42.51144145, 37.29525277, 33.13879613, 33.13058898,\n",
+    "       27.6504018 , 32.00276581, 37.19959292, 31.64418287, 37.85890413,\n",
+    "       50.64455723, 53.26189276, 44.16929211, 49.08965557, 42.36165314,\n",
+    "       39.82915646, 44.60481335, 38.69534586, 42.77630437, 47.59814922,\n",
+    "       48.71239135, 50.36431533, 51.88045129, 52.89177835, 49.29070895,\n",
+    "       53.57511182, 44.31260359, 50.20744251, 41.37338282, 46.03992489,\n",
+    "       46.97807129, 47.75535531, 49.97741155, 54.30628047, 57.65448273,\n",
+    "       57.44782946, 49.09053715, 49.81219695, 46.72417144, 46.0311564 ,\n",
+    "       45.04752322, 44.60645396, 47.17987572, 52.30262242, 48.06727738,\n",
+    "       57.13731006, 56.26380265, 57.83153857, 56.75468076, 54.71784953,\n",
+    "       51.742382  , 47.45045687, 41.79072237, 49.53881885, 50.13675035,\n",
+    "       52.600372  , 54.61455211, 58.88523455, 57.86827682, 58.4039786 ,\n",
+    "       56.33625311, 55.87615159, 54.03660964, 44.936906  , 51.49690003,\n",
+    "       47.19675691, 51.27874422, 52.12747669, 53.68196493, 57.03014303,\n",
+    "       58.13588317, 60.45585654, 55.91280909, 49.46168341, 54.35719872,\n",
+    "       49.61442916, 49.04766705, 48.65478854, 50.32448206, 59.89094014,\n",
+    "       58.9865658 , 58.86774878, 52.87718759, 55.28051877, 60.92536613,\n",
+    "       55.92265   , 46.16703477, 49.6700841 , 52.35785335, 50.59231923,\n",
+    "       51.59957118, 55.84467833, 60.15888731, 63.27692528, 62.84108402,\n",
+    "       60.74284195, 61.88019549, 53.30303293, 57.45246017, 48.85485823,\n",
+    "       51.76801057, 50.73668764, 52.82848916, 55.19734302, 60.2669935 ,\n",
+    "       66.38470358, 59.73272987, 62.27745923, 62.432064  , 54.43587371,\n",
+    "       54.85934313, 46.43824379, 57.96466491, 55.77932624, 60.60174465,\n",
+    "       59.65298416, 56.62211788, 67.36302633, 66.27148442, 57.95151434,\n",
+    "       63.74746697, 52.54036198, 50.40561031, 59.24355332, 56.80434129,\n",
+    "       55.95082734, 60.31745782, 60.39747463, 60.4767769 , 62.5935966 ,\n",
+    "       59.81834154, 58.70264969, 63.8950646 , 57.36403968, 51.81715415,\n",
+    "       58.33809794, 51.12632774, 57.49079346, 63.48887669, 59.52374639,\n",
+    "       67.49671164, 62.28309287, 65.66098114, 64.65925981, 66.81293494,\n",
+    "       61.19720923, 55.48751692, 51.60104992, 53.96826095, 56.58452635,\n",
+    "       59.34553001, 67.65229591, 65.75424793, 65.65632486, 65.99148085,\n",
+    "       65.44853708, 60.50694571, 61.19993821, 60.12364375, 59.93184649,\n",
+    "       63.48731119, 61.12350502, 58.24920452, 65.8986243 , 63.64536901,\n",
+    "       63.79572593, 64.12001903, 62.14535278, 64.68425609, 56.90342646,\n",
+    "       61.57831818, 57.66321622, 63.72222826, 61.48417245, 66.02904428,\n",
+    "       65.32289511, 72.29778331, 69.87048382, 66.83200272, 62.6373197 ,\n",
+    "       61.73645523, 65.08488989, 61.04227702, 60.53522292, 55.77257358,\n",
+    "       65.45566672, 68.37357759, 71.94120313, 68.90744536, 68.54569404,\n",
+    "       69.92220244, 66.03327737, 58.38590875, 60.85068801, 58.80399466,\n",
+    "       66.63495512, 65.61470701, 67.62668741, 65.90517767, 70.52692682,\n",
+    "       69.66761933, 73.51037934, 67.74721527, 63.02629107, 62.17487742,\n",
+    "       61.19369148, 63.30270982, 61.92753578, 66.46761908, 67.8833176 ,\n",
+    "       68.33023079, 76.04517509, 74.44534732, 69.34495782, 69.45929329,\n",
+    "       65.25027491, 66.75228573, 60.42055774, 68.7378364 , 61.57801931,\n",
+    "       68.88936407, 65.84014135, 72.20553655, 74.03307371, 75.53317529,\n",
+    "       69.83744227, 69.6454565 , 67.24104963, 64.54883909, 61.37027826,\n",
+    "       68.15912383, 67.884073  , 67.77746262, 67.56149168, 68.15165627,\n",
+    "       65.45634875, 81.10033402, 73.18088532, 72.79922135, 70.53731861])\n",
     "t = np.arange(0, len(data), 1)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -61,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -102,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -209,12 +313,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1200x500 with 2 Axes>"
       ]
diff --git a/book/time_series/forecasting.md b/book/time_series/forecasting.md
index 595c8c6f..87856274 100644
--- a/book/time_series/forecasting.md
+++ b/book/time_series/forecasting.md
@@ -24,8 +24,11 @@ In summary, given a time series $Y=\mathrm{Ax}+\epsilon$, the workflow is as fol
 
 3. Predict the signal-of-interest: $\hat{Y}_{signal}=\mathrm{A}_p\hat{X}$, where $\mathrm{A}_p$ is the design matrix describing the functional relationship between the future values $Y_p$ and $\mathrm{x}$.
 
-4. Predict the noise $\hat{\epsilon}_p$ based on the AR model.
+4. Predict the noise $\hat{\epsilon}_p$ based on the AR model. $\hat{\epsilon}_p = \Sigma_{Y_pY}\Sigma_Y^{-1}\hat{\epsilon}$, where $\Sigma_{Y_pY}$ is the covariance matrix between the future values $Y_p$ and the observed values $Y$.
 
 5. Predict future values of the time series: $\hat{Y}_p=\mathrm{A}_p\hat{X}+\hat{\epsilon}_p$.
 
+```{note}
+This procedure is a general approach to forecasting time series data. It resembles the process of stochastic inter- and extrapolation, which is used in many fields of science and engineering.
+```
 
diff --git a/book/time_series/intro.md b/book/time_series/intro.md
index 8b8c6dc6..b761b924 100644
--- a/book/time_series/intro.md
+++ b/book/time_series/intro.md
@@ -9,5 +9,5 @@ Next, we will consider stationary time series, meaning that the statistical prop
 :width: 600px
 :align: center
 
-Recorded and expected global warming from 1960 to 2100 ([Huseien, Shah (2021)](https://www.mdpi.com/2071-1050/13/17/9720))
+Recorded and expected global warming from 1960 to 2100, from IPCC report ([Masson-Delmotte, et al. (20219)](https://www.researchgate.net/profile/Peter-Marcotullio/publication/330090901_Sustainable_development_poverty_eradication_and_reducing_inequalities_In_Global_warming_of_15C_An_IPCC_Special_Report/links/6386062b48124c2bc68128da/Sustainable-development-poverty-eradication-and-reducing-inequalities-In-Global-warming-of-15C-An-IPCC-Special-Report.pdf))
 ```
diff --git a/book/time_series/modelling.md b/book/time_series/modelling.md
index c391c6f1..28868129 100644
--- a/book/time_series/modelling.md
+++ b/book/time_series/modelling.md
@@ -27,11 +27,11 @@ Recall that the BLUE of $\mathrm{x}$ is:
 
 $$\hat{X}=(\mathrm{A}^T\Sigma_{Y}^{-1}\mathrm{A})^{-1}\mathrm{A}^T\Sigma_{Y}^{-1}Y,\hspace{10px}\Sigma_{\hat{X}}=(\mathrm{A}^T\Sigma_{Y}^{-1}\mathrm{A})^{-1}$$
 
-The BLUE of $Y$ and $\epsilon$ is
+The BLUE of $Y$  is:
 
 $$\hat{Y}=\mathrm{A}\hat{X},\hspace{10px}\Sigma_{\hat{Y}}=\mathrm{A}\Sigma_{\hat{X}}\mathrm{A}^T$$
 
-and
+and $\epsilon$ is:
 
 $$\hat{\epsilon}=Y-\hat{Y},\hspace{10px}\Sigma_{\hat{\epsilon}}=\Sigma_{Y}-\Sigma_{\hat{Y}}$$
 
diff --git a/book/time_series/noise.ipynb b/book/time_series/noise.ipynb
index ba321f6b..d8296c51 100644
--- a/book/time_series/noise.ipynb
+++ b/book/time_series/noise.ipynb
@@ -24,7 +24,7 @@
     "\n",
     "* **Signal** - the meaningful information that we want to detect: deterministic characteristics by means of mathematical expressions to capture for example trend, seasonality and offsets.\n",
     "\n",
-    "* **Noise** - random and undesired fluctuation that interferes with the signal: stochastic process are needed to describe this. Parts of the time-correlated noise  needs to be accounted for in prediction, see later {ref}`AR`. \n",
+    "* **Noise** - random and undesired fluctuation that interferes with the signal: stochastic process are needed to describe this. Parts of the time-correlated noise  needs to be accounted for in predictions, see later {ref}`AR`. \n",
     "\n",
     "The example in {numref}`signal_noise` shows that the *signal* can be described by $\\cos(2\\pi t f) + \\sin(2\\pi t f)$. The stochastic model (assuming independent normally distributed observations) would be a scaled identity matrix with variance equal to 1 (middle panel) and 9 (bottom panel), respectively. The signal of interest has been entirely hidden in the background noise in the bottom panel. Techniques from signal processing can be used to detect the frequency.\n",
     "\n",
@@ -61,7 +61,7 @@
     "Most notable, all observations are uncorrelated (off-diagonal elements of the covariance matrix are equal to 0). When we compute the PSD, the resulting density will be flat over the entire range of frequencies. In other words, a white noise process has equal energy over all frequencies, just like white light. We will show this in the interactive plot at the bottom of this page.\n",
     "\n",
     "### Colored noise\n",
-    "In time series it is not guarantied that the individual observations are uncorrelated. At the bottom of this page you will find an interactive plot. You can select four different types of noise: white, pink, red and blue. The noise processes are plotted in combination with the PSD. The PSD #TODO(add ref to psd)is a measure of the power of the signal at different frequencies. The white noise process has a flat PSD, while the other noise processes have a different shape. The pink noise process has a PSD that decreases with frequency, the red noise process has a PSD that decreases quadratically  with frequency, and the blue noise process has a PSD that increases with frequency. \n",
+    "In time series it is not guarantied that the individual observations are uncorrelated. At the bottom of this page you will find an interactive plot. You can select four different types of noise: white, pink, red and blue. The noise processes are plotted in combination with the PSD. The [PSD](../signal/spectral_est.md#power-spectral-density-psd) is a measure of the power of the signal at different frequencies. The white noise process has a flat PSD, while the other noise processes have a different shape. The pink noise process has a PSD that decreases with frequency, the red noise process has a PSD that decreases quadratically  with frequency, and the blue noise process has a PSD that increases with frequency. \n",
     "\n"
    ]
   },
diff --git a/book/time_series/notebook.ipynb b/book/time_series/notebook.ipynb
deleted file mode 100644
index 69fdcf74..00000000
--- a/book/time_series/notebook.ipynb
+++ /dev/null
@@ -1,800 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "144c3547",
-   "metadata": {},
-   "source": [
-    "# TSA Notebook\n",
-    "\n",
-    "MMMMM from 231129 ipynb from Alireza: move these to the right page, or keep all here? If on other page, put under a second-level heading, as above\n",
-    "\n",
-    "With this notebook, you can practice the lectures/videos material. First, you need to study the lecture slides and/or watch the pre-recorded videos. Having studied the lecture material, you are ready to implement your knowledge and practice by doing this notebook.\n",
-    "Enjoy Time Series Analysis!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "6f142ea1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: statsmodels in c:\\users\\aamirisimkooei\\anaconda3\\lib\\site-packages (0.12.2)\n",
-      "Requirement already satisfied: numpy>=1.15 in c:\\users\\aamirisimkooei\\anaconda3\\lib\\site-packages (from statsmodels) (1.20.3)\n",
-      "Requirement already satisfied: scipy>=1.1 in c:\\users\\aamirisimkooei\\anaconda3\\lib\\site-packages (from statsmodels) (1.7.1)\n",
-      "Requirement already satisfied: pandas>=0.21 in c:\\users\\aamirisimkooei\\anaconda3\\lib\\site-packages (from statsmodels) (1.3.4)\n",
-      "Requirement already satisfied: patsy>=0.5 in c:\\users\\aamirisimkooei\\anaconda3\\lib\\site-packages (from statsmodels) (0.5.2)\n",
-      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\aamirisimkooei\\anaconda3\\lib\\site-packages (from pandas>=0.21->statsmodels) (2.8.2)\n",
-      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\aamirisimkooei\\anaconda3\\lib\\site-packages (from pandas>=0.21->statsmodels) (2021.3)\n",
-      "Requirement already satisfied: six in c:\\users\\aamirisimkooei\\anaconda3\\lib\\site-packages (from patsy>=0.5->statsmodels) (1.16.0)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Import the necessary libraries:\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "!pip install statsmodels\n",
-    "#from statsmodels.tsa.stattools import adfuller\n",
-    "import scipy.signal\n",
-    "from statsmodels.graphics.tsaplots import plot_acf\n",
-    "from scipy import signal"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "374be44f",
-   "metadata": {},
-   "source": [
-    "### Exercise 1. Components of time series  (Video 1) \n",
-    "\n",
-    "**Introduction:** The four components of time series are the trend, seasonality, offset, and noise (white/colored). We use simulated data to show these components here. The observation equation of time series should have the following mathematical representation:\n",
-    "$$Y(t) = y_0 + r t + a \\cos(\\omega_ot) + b\\sin(\\omega_ot) + o {u_k(t)} + \\epsilon(t)= y_0 + r t + A_m \\sin(\\omega_o t+\\phi_0) + o {u_k(t)} + \\epsilon(t)$$\n",
-    "where\n",
-    "- $y_0 $: intercept (e.g. in mm)\n",
-    "- $r$: is the rate (e.g. in mm/day)\n",
-    "- $a$ and $b$ are the coefficients of the periodic signal \n",
-    "- $\\omega$ is the frequency of signal (e.g. cycle/ day)\n",
-    "- $o$ is the size of the offset at time instant $t_k$\n",
-    "- $\\epsilon(t)$ is the random noise with a given variance which follows a Normal distribution: $ \\epsilon(t) \\sim \\textbf{N}(0, \\sigma^2)$\n",
-    "\n",
-    "Here, we are assuming only a single seasonality and offset component. However, in many practical scenarios, there could be multiple components related to these.\n",
-    "\n",
-    "**Exercise:**\n",
-    "You can simulate your time series based on the priori information provided in the scripts. Plot your results and change the input variables to see the effect.\n",
-    "\n",
-    "*The noise follows a normal distribution: use np.random.normal in order to draw random samples from a normal (Gaussian) distribution. Study more for this function here: [normal distribution in python](https://numpy.org/doc/stable/reference/random/generated/numpy.random.normal.html)*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "13f99024",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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zz3DXXfDYY+GO8bPPhhtugGbNoq5MspCCW0SkNHPnwsCB4VnswkI4/3zo0wf22ivqyiSLKbhFRIqbPTuszvXUU+ExrosuCoGdmxt1ZSIKbhGRP8ycCbffDqNGhWlIu3aFXr2gceOoKxP5g4JbRGTq1LBS19ixULMmXHMNXHst7Lhj1JWJ/ImCW0Sy1+TJIbBffjlMS3rDDXD11VC/ftSViZRKwS0i2eff/4Z+/eBf/4LttoNbboGrroJ69aKuTGSzFNwikh3cYcKEENgTJ0KDBuGO8a5doU6dqKsTKTMFt4hkNvcwsu7XD/7zH2jUKExResklUKtW1NWJbDEFt4hkJvdw7fq222DKFNhlFxgyJDzaVaNG1NWJlFvK1+MWEalUGzfC88+HaUhPPz0s/vHEE+HZ7K5dFdoSewpuEckMGzbAM89A8+Zw5plhatKnnoKvvoKLLw7PZYtkAAW3iMSarV8PQ4fCPvuEKUmrV4fnnoPp08PraroiKJlF/0WLSDytXQvDhnHQLbeEVbtat4aXXgpLbFbRmEQyl4JbROJl9eqwStddd8G8eaxr1owaw4bB8ceHecVFMpyCW0Ti4ddfwzrY99wDv/wCbdrA00/zmRlt2raNujqRlNH5JBFJb8uXh2ewc3PDCl2tWsH774fJVI46SqNsyToacYtIelq8GAYNggcfhJUr4dRT4cYb4b//O+rKRCKl4BaR9LJgQTgd/vDD4Xp2+/YhsFu0iLoykbSg4BaR9PDzz3DnneHGs99+g3POCat1NW0adWUiaUXBLSLRmjMnLPYxbBgUFoZnr6+/HvbcM+rKRNKSgltEovHNNzBgADz9dHju+qKL4Lrrwk1oIlIqBbeIpNaMGXD77ZCXF6Yh7dYNevaExo2jrkwkFhTcIpIan38eVup64QWoWROuvTZ85eREXZlIrCi4RaRyffJJCOxXXoG6dcMd4t27Q/36UVcmEksKbhGpHB98ECZOefNN2H57uPVWuPJKqFcv6spEYk3BLSLJ4w7vvhsCe9IkaNgQ7rgDLr8c6tSJujqRjKDgFpGKc4c33giB/eGH0KgR3HcfdOkSrmeLSNIouEWk/AoL4eWXwzXs/HzYddewEMiFF0KNGlFXJ5KRNhncZlYDOBk4HNgJWAN8Cbzq7tMrvzwRSUsbN8LYsSGwp02D3XeHoUOhY8fwiJeIVJpSg9vMbgZOASYCHwOLgBrAXsDARKhf6+5fVH6ZIpIWNmyAUaOgf3+YNQv22SdMoHL22VBNJ/BEUmFT/6dNdvebS9l3r5k1BHYtz4eaWT3gCaA54MBF7v5heY4lIinw22/w1FNhprPvvoP994fRo6FdO6haNerqRLJKqetxu/urAGbWofg+M+vg7ovcfUo5P/d+4A133wc4AJhZzuOISGVauzZcs95zT7jkkvBY17hx8Nln0KGDQlskAqUGdxHXl3FbmZhZXeAIYCiAu//m7svLezwRqQSrV4e7wnfbLUxJ2rgxvP56mEzl1FPD3OIiEglz95J3mJ0AnAicCTxXZFddoKm7l2s1ezNrATwGzCCMtvOB7u6+qtj7ugBdAHJyclrn5eWV5+NKVFBQQO3atZN2vGykHiZHuvWx6urV7PzSSzR+/nm2Wr6cZS1bMvf881neogWYRV1eidKth3GkHlZcsnvYtm3bfHc/sKR9mwruA4CWwC3AP4vs+hWY4O7LylOMmR0IfAQc6u4fm9n9wEp3/0dpP3PggQf6lCnlPSv/ZxMnTqRNmzZJO142Ug+TI236uHw5PPAADBoEy5bB8cdD375w6KFRV7ZZadPDGFMPKy7ZPTSzUoO71JvT3H0qMNXMRrr7+qRVAz8BP7n7x4nXY4A+STy+iJTV4sXhlPjgwbByZTgN3rcv/Nd/RV2ZiJSi1AtVZvaKmZ1Syr7dzOxWM7toSz/Q3RcAP5rZ3olNRxNOm4tIqixYAL16hbWvBwyAv/41rN41bpxCWyTNbepxsEuAa4D7zGwZ8AuwDZALzAYGu/u4cn7ulcBIM9sK+A64sJzHEZEt8dNPcOed8Pjj4RGvc8+F66+Hpk2jrkxEymhTp8oXAL3N7EfgA8LkK2uAr919dUU+1N0/B0o8dy8ileD772HgQBg2LMwrfsEFIbD32CPqykRkC5VlqqMc4HngU+BJYGqlViQiyfPNN2GWs6efDs9c//3v0Lt3OEUuIrG02Ycx3b0vsCfhuevOwDdm1t/Mdq/k2kSkvKZPD6fB99kH8vLgiivCjGcPPaTQFom5Ms2i4OGZsQWJrw3AdsAYM7uzEmsTkS312WfQvj00bx5W7erZE+bMCY957bxz1NWJSBJs9lS5mV0FdAIWE+YX7+Xu682sCvAN0LtySxSRzfrkk7AW9vjxULdueKTr6qthhx2irkxEkqws17jrA+3cfW7Rje5eaGYnV05ZIlIm778fAvutt8I84v36hdPi9epFXZmIVJLNBre7/3MT+7Q4iEiqucO774aQnjQJGjYMj3hdfjlo2kqRjKcFdEXiwj0s9NGvH3z0Eey0U7h2fcklULNm1NWJSIpoiR+RdFdYCC++CAceCCedBPPnw8MPh7vEu3dXaItkGQW3SLrauBGeew4OOADatQtziT/5ZHg2+7LLYOuto65QRCKg4BZJNxs2wFNPQbNmcPbZIcCfeQZmzoQLL4Tq1aOuUEQipGvcIunit99gxIgwNel334WR9vPPh9F2Ff2OLSKB/jUQidratTBkSJg3vEuX8FjXuHFhMpUzzlBoi8j/oxG3SFRWraLx6NFwzjlhmc1DDw2rdh13HJhFXZ2IpCkFt0iqrVwZ5gy/5x72WLwYjjoKRo2CI49UYIvIZim4RVJl2TJ44AG4//7w/Qkn8OmJJ9LqiiuirkxEYkQXz0Qq2y+/wA03wF/+AjffDEccAZMnw2uvsbJ586irE5GY0YhbpLLMnw/33BMmS1mzBjp0gBtvhP33j7oyEYkxBbdIsv34Y5g7/PHHYf36sC72DTfAvvtGXZmIZAAFt0iyfP89DBgAw4eHecU7dYI+fcJjXiIiSaLgFqmor7+G/v3D7GZVq4ZFP3r3Dte0RUSSTMEtUl5ffgm33w6jR4d5w6+8Enr1Cqt2iYhUEgW3yJb67DO47TZ44YWw/nWvXnDNNWFdbBGRSqbgFimrjz8Oa2G/+ipsuy384x9hWc0ddoi6MhHJIgpukc15770Q2G+/HUL6ttvgiitCeIuIpJiCW6Qk7vDOOyGw33sPcnLgrrvCOti1a0ddnYhkMQW3SFHu8NprIbA//hh23jlMUXrJJbDNNlFXJyKiKU9FACgsDDebtW4NJ58MCxfCI4/At9/CVVcptEUkbSi4Jbtt3Ah5eXDAAdC+PRQUwLBh4dnsSy8Nj3mJiKQRBbdkp/XrYcQIaNo0rIddWAgjR8KMGdC5M1SvHnWFIiIl0jVuyS7r1oXAHjgwTFF6wAEwZgz87W9QRb/Hikj6i+xfKjOramafmdn4qGqQLLJmDQweHOYNv/RSqF8fXn45TKbSvr1CW0RiI8oRd3dgJlA3whok061aFW4yu/tuWLAADjsMhg6FY48Fs6irExHZYpEMM8ysMXAS8EQUny9ZYOXKsFJXbi707AnNmsGECeGZ7OOOU2iLSGxFNeIeBPQG6kT0+ZKpli0Lz13ffz8sXw4nngh9+8Ihh0RdmYhIUpi7p/YDzU4GTnT3rmbWBujp7ieX8L4uQBeAnJyc1nl5eUmroaCggNqa/apC0q2H1Zcvp/Hzz7PzSy9RbfVqfjnsMOZ27EjB3ntHXdompVsf40g9rDj1sOKS3cO2bdvmu/uBJe5095R+AQOAn4A5wAJgNfDMpn6mdevWnkwTJkxI6vGyUdr0cN4892uuca9Z093M/ayz3KdOjbqqMkubPsaYelhx6mHFJbuHwBQvJRNTfo3b3a9398bungucDbzr7h1TXYfE3I8/hoU+mjQJp8Xbtw/PYOflwf77R12diEil0XPcEi/ffReewR4+PLzu1An69IHdd4+0LBGRVIk0uN19IjAxyhokJr76Cvr3D7ObVasWFv247jrYddeoKxMRSSmNuCW9ffllWP969GioUSMs+NGzJ+y0U9SViYhEQsEt6enTT0Ngv/hiWP/6uuugRw9o2DDqykREIqXglvTy0UdhLezXXoNtt4V//hO6d4ftt4+6MhGRtKDglvQwaVIYYb/9NuywA9x+O3TrFsJbRET+oOCW6LiHoO7XD95/H3Jywpzil14aTo+LiMifKLgl9dzh1VfDCPvjj2HnneGBB+Dvf4dttom6OhGRtKa1DCV1Cgth7Fho1QpOOQUWLoRHH4Vvv4Urr1Roi4iUgYJbKt/GjTBqVJjR7IwzwlKbw4bB119Dly6w9dZRVygiEhsKbqk869eHGc723RfOPTecIn/2WZg5Ezp3hurVo65QRCR2dI1bkm/dOhgxIqyHPWcOtGgBY8bA3/4GVfS7oohIRehfUUmeNWvgwQdhjz3CneENG8Irr4TJVNq3V2iLiCSBRtxScQUF8Mgj4VGuhQvh8MPhySfhmGPALOrqREQyioJbym/lShg8GO69F5YsCUH93HNw5JFRVyYikrEU3LLlli4ld9iwcM16+XI48UTo2xcOOSTqykREMp6CW8pu0SK47z4YPJjcgoIQ3H37hueyRUQkJRTcsnnz58Ndd4Xr2GvXwplnMvm44/iviy6KujIRkayj23yldD/8EBb6aNIkTEnaoUN4Bjsvj1W77RZ1dSIiWUkjbvmz774Lz2CPGBFed+4MffqAwlpEJHIKbvk/s2ZB//5hdrNq1cJ0pL17w667Rl2ZiIgkKLgFpk0L61+PHh0W+ujeHXr2hEaNoq5MRESKUXBns/z8sLTmSy9BnTrhdHiPHtCgQdSViYhIKRTc2ejDD6FfP3j9dahXD266Ca66CrbfPurKRERkMxTc2cIdJk0KI+x33oH69cP17G7doG7dqKsTEZEyUnBnOnd4660wwv7gA8jJCXOKX3YZ1KoVdXUiIrKFFNyZyh3Gjw8j7E8+gcaNw8pdF18cbkATEZFY0gQsmaawEMaODdOQnnoq/PILPPYYzJ4NV1yh0BYRiTkFd6bYuDE8f73ffnDGGbB6NQwfDl99BZdcAltvHXWFIiKSBAruuFu/HoYNg333hfPOC+tfjxoFM2ZAp05QvXrUFYqISBLpGndcrVsXRtQDB8KcOdCyZThFfvrpUEW/j4mIZCr9Cx83a9aEBT923z3cGZ6TE25Cy8+Hdu0U2iIiGU4j7rgoKAjLat59NyxcCEccEUbcRx8dTo+LiEhWSPnwzMx2MbMJZjbTzKabWfdU1xArK1aEecRzc6FXL9h//zCRyqRJcMwxCm0RkSwTxYh7A3Ctu39qZnWAfDN7y91nRFBL+lq6FAYNCqfFV6yAk06Cvn3h4IOjrkxERCKU8uB29/nA/MT3v5rZTGBnQMENsGgR3HsvDBkSTo+3axcCu2XLqCsTEZE0YO4e3Yeb5QLvAc3dfWWxfV2ALgA5OTmt8/Lykva5BQUF1K5dO2nHS4atFi9ml+eeY6dXXqHKb7+xqG1bfujYkVVNmkRdWonSsYdxpD5WnHpYcephxSW7h23bts139wNL3OnukXwBtYF8oN3m3tu6dWtPpgkTJiT1eBUyZ4775Ze7b7WVe9Wq7p06uc+aFXVVm5VWPYwx9bHi1MOKUw8rLtk9BKZ4KZkYyV3lZlYdGAuMdPcXoqghct9+CwMGwIgR4Qazzp3Deti77RZ1ZSIiksZSHtxmZsBQYKa735vqz4/crFnhLvFnnw2zml12GfTuDbvsEnVlIiISA1HM1nEocD5wlJl9nvg6MYI6UuuLL+Css6BpU3jhBejRA77/PqzYpdAWEZEyiuKu8g+A7Hn4OD8/rIU9bhzUqRNOh/foAQ0aRF2ZiIjEkGZOqyz/+U9YC/v116FePbj5ZrjqKthuu6grExGRGFNwJ5M7TJwYAvvdd6F+/XADWteuULdu1NWJiEgGUHAngzu8+WY4Jf7vf8OOO8I998Cll0KtWlFXJyIiGUTBXRHu8MorYYQ9eXK4yWzwYLj4YqhRI+rqREQkA2kNyPIoLIQxY8I0pKedBosXw2OPwezZ0K2bQltERCqNgntLbNgAI0dC8+bQoUNYG3vECPj6a7jkEthqq6grFBGRDKfgLov162HYMNh3X+jYEapWhbw8mDEDLrgAqumKg4iIpIYSZ1PWrQuBPXAgzJ0LrVqFyVNOOw2q6HceERFJPQV3SVavhscfhzvvhHnzwhrYDz0EJ5wQ5hUXERGJiIK7qIICePhhuPvusC72kUfCU0/BUUcpsEVEJC0ouAFWrAhzht93HyxdCsceC//4Bxx+eNSViYiI/D/ZHdxLlsCgQSG0V6yAk0+Gvn3hoIOirkxERKREWRnc1Zctg+uuC9etCwqgXbsQ2C1bRl2aiIjIJmVfcD/4IAf36hUe8TrrLLjxRmjWLOqqREREyiT7gjs3l0Vt29Lo/vthr72irkZERGSLZF9wn3IKX9WpQyOFtoiIxJBmEREREYkRBbeIiEiMKLhFRERiRMEtIiISIwpuERGRGFFwi4iIxIiCW0REJEYU3CIiIjFi7h51DZtlZr8Ac5N4yPrA4iQeLxuph8mhPlacelhx6mHFJbuHf3H3BiXtiEVwJ5uZTXH3A6OuI87Uw+RQHytOPaw49bDiUtlDnSoXERGJEQW3iIhIjGRrcD8WdQEZQD1MDvWx4tTDilMPKy5lPczKa9wiIiJxla0jbhERkVhScIuIiMRI1gW3mR1vZl+Z2Wwz6xN1PenKzJ40s0Vm9mWRbdub2Vtm9k3iz+2K7Ls+0dOvzOyv0VSdXsxsFzObYGYzzWy6mXVPbFcfy8jMapjZJ2Y2NdHDWxLb1cMtZGZVzewzMxufeK0ebgEzm2Nm08zsczObktgWSQ+zKrjNrCowBDgBaAqcY2ZNo60qbQ0Hji+2rQ/wjrvvCbyTeE2ih2cDzRI/81Ci19luA3Ctu+8LHAx0S/RKfSy7dcBR7n4A0AI43swORj0sj+7AzCKv1cMt19bdWxR5XjuSHmZVcAP/Dcx29+/c/TcgDzgt4prSkru/Bywttvk0YETi+xHA6UW257n7Onf/HphN6HVWc/f57v5p4vtfCf9o7oz6WGYeFCReVk98OerhFjGzxsBJwBNFNquHFRdJD7MtuHcGfizy+qfENimbHHefDyGUgIaJ7errZphZLtAS+Bj1cYskTvF+DiwC3nJ39XDLDQJ6A4VFtqmHW8aBN80s38y6JLZF0sNqyTpQTFgJ2/Q8XMWpr5tgZrWBscDV7r7SrKR2hbeWsC3r++juG4EWZlYPeNHMmm/i7ephMWZ2MrDI3fPNrE1ZfqSEbVndw4RD3X2emTUE3jKzWZt4b6X2MNtG3D8BuxR53RiYF1EtcbTQzBoBJP5clNiuvpbCzKoTQnuku7+Q2Kw+loO7LwcmEq4ZqodldyhwqpnNIVwePMrMnkE93CLuPi/x5yLgRcKp70h6mG3BPRnY08yamNlWhJsHXo64pjh5GeiU+L4TMK7I9rPNbGszawLsCXwSQX1pxcLQeigw093vLbJLfSwjM2uQGGljZtsAxwCzUA/LzN2vd/fG7p5L+DfvXXfviHpYZmZWy8zq/P49cBzwJRH1MKtOlbv7BjO7AvgXUBV40t2nR1xWWjKzUUAboL6Z/QTcBAwERpvZxcAPQAcAd59uZqOBGYQ7qbslTm9mu0OB84FpiWu0ADegPm6JRsCIxB25VYDR7j7ezD5EPawo/XdYdjmEyzQQcvNZd3/DzCYTQQ815amIiEiMZNupchERkVhTcIuIiMSIgltERCRGFNwiIiIxouAWERGJEQW3SMyY2Q6JFYo+N7MFZvZz4vsCM3uokj7zajO7oITtuVZkBbktPOZ+Zja8wsWJZJmseo5bJBO4+xLCSlmY2c1AgbvfXVmfZ2bVgIuAVsk8rrtPM7PGZraru/+QzGOLZDKNuEUyhJm1KbLW8s1mNsLM3kysI9zOzO5MrCf8RmIqVsystZlNSiyc8K/fp28s5ijgU3ffUORnpiYmQelW5PNzzex9M/s08fU/ie1Pm9lpRd430sxOTbx8hTCbl4iUkYJbJHPtTljK8TTgGWCCu+8HrAFOSoT3g8AZ7t4aeBK4vYTjHArkF3k9DLjK3Q8p9r5FwLHu3go4C3ggsf0J4EIAM9sW+B/gtcS+KcDhFflLimQbnSoXyVyvu/t6M5tGmOL3jcT2aUAusDfQnLDSEYn3zC/hOI0Ia4n/Hrz13H1SYt/TwAmJ76sDg82sBbAR2AvA3SeZ2ZDEqkrtgLG/j94JYb9TUv62IllCwS2SudYBuHuhma33/5vfuJDw/74B00sYORe3BqiR+N4ofXnCHsBC4ADC2by1RfY9DZxHOC1+UZHtNRLHF5Ey0qlykez1FdDAzA6BsASpmTUr4X0zgT3gj6U1V5jZYYl95xV537bAfHcvJCyuUrXIvuHA1YljFF3YZy/CKksiUkYKbpEs5e6/AWcAd5jZVOBzwvXn4l4Hjijy+kJgSOLmtKKj5YeATmb2ESGQVxX5rIWEXwCGFTt2W+DViv1NRLKLVgcTkc0ysxeB3u7+TTl/vibh2nord1+R2LY1MAk4rMg1bxHZDI24RaQs+hBuUttiZnYMMAt48PfQTtgV6KPQFtkyGnGLiIjEiEbcIiIiMaLgFhERiREFt4iISIwouEVERGJEwS0iIhIj/wt9YyNKhzkjmgAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 576x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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w/7b5FMlXhNZjWtNtcjd2HdoVtDZnbZpFwocJ5MuTjzk951CzZM2gtRUsKtwiIhHKOUfvqb1Z8McChlw3hJvrefMoiYvKX8SSPksY0HwAE1ZNoNaQWoxZMSbgl44NWTiEq0ZexTmFzmFuz7lUK1EtoOsPFRVuEZEINSBxAB8u/ZCeFXpyZ+M7PY0lb3ReBiYMZHHvxVQqWomuk7vSZmwbftn3S47XvffwXrpN7sY90+7h2mrXMr/XfCoVqxSAqL2hwi0iEoE+Xvsx/577b25rcBvdK3T3OpyT6sbX5bte3/HKVa8wc+NMqr9ZnXs+v4etB7ZmeV2pLpXhS4dTe0htxq8az9MJT/PxjR+HzejxjKhwi4hEmPV71tPj4x5ceM6FDLluSLav0Q6W6KhoHrjkAdb2XcutDW5l6JKhVPxPRTqM78C0DdM4nnL8tL+/78g+/rv4v9R9uy49P+nJuUXO5fvbv+fJ5k+GzX3TT0f3KhcRiSBJx5JoN64deaPzMrHzRPLlyed1SBk6r8h5vNP6HR5p+ghv/vAmI5aPYPKaycTljeOy8y6jbum6nFvkXArEFODg0YP8uv9XFm9fzLzf5pGcmkzDMg0Z02EMnWt3PisK9glBK9xmNgxoDex0ztU5Zd5DwEtAKefc7mDFICIi/+Oco9envVi7ey1f3/w15xU5z+uQMqVSsUq80uoVnr/ieb7Y8AVf/vQl3/72LTM3zeRYyrGTy8XmiaVu6bo8cPEDtD+/PU3KNcl1vQmBEMwj7g+BN4ERaSea2bnAlcCvQWxbREROMXjBYMavGs+gloNoWbml1+FkWd7ovLSt2Za2NdsCkJyazN7De/nz+J8UyleIIvmKhMUtS3MqaIXbOTfXzCqmM+s14GHgk2C1LSIifzVn8xz+Nf1ftKvZjoebPux1OAGRJyoPpQqW8jqMkAtpp7+ZtQG2OueWhbJdEZFItvXAVjpP7EzV4lX58IYPz8ru40hiwXw2qv+Ie6pzro6ZFQBmA1c55/ab2WagcUbnuM2sN9AbID4+vtHYsWMDFldSUhJxcXEBW18kUg4DQ3nMOeXw9I6nHuf+Zffzc9LPvH3B21QsWPFvyyiHORfoHLZo0WKxc65xevNCOaq8ClAJWOb/b688sMTMmjjndpy6sHNuKDAUoHHjxi4hISFggSQmJhLI9UUi5TAwlMecUw5Pr++0vqw6sIrxHcfTqXandJdRDnMulDkMWeF2zq0ASp94faYjbhERyZmRy0by1sK3ePCSBzMs2hJ+gnaO28zGAN8BNcxsi5n1ClZbIiLyV0t3LKX31N4kVExg0BWDvA5HAiiYo8pvOsP8isFqW0Qkku09vJf249pTIn8JxnYYS54o3WvrbKJ3U0TkLJLqUrl5ys1sObCFubfOJT4u3uuQJMBUuEVEziIDEwcybcM03rr2LS4uf7HX4UgQqHCLSMTYd2QfszfNZt5v8/jpj5/Y9ecuUl0qxWKLUaFIBS4sdyEJFROoXKyy16Fmy+Q1k08+8euuxnd5HY4EiQq3iJz1FmxZwOAFg/l47cccTTlKbJ5YqhSrQnxcPFEWxY6kHcz/bT7vLH4HgMbnNObG2jdyW8PbKJa/mMfRZ87KnSvpPqU7F5W7iLeue0s3WTmLqXCLyFlrw54N/POrfzJtwzSKxRajT6M+dKzVkYvKX0Te6Lx/WTbVpbJ+z3qmrp/KuFXjeGj6QwycM5A7G93Jg5c+SJm4Mh5txZntPbyXG8beQKF8hZjUeRKxeWK9DkmCSIVbRM46qS6Vl+e/zP/N/j9i88QyqOUg7mlyD3F5M76zVZRFUbNkTWqWrMlDlz7E8t+X88K8F3h1wau8vehtHrvsMe6/+H7yx+QP4Zac2dHko7Qf355f9/9KYs9EyhUu53VIEmRnzwNKRUTwHX22HduWR2Y8QuvqrVlzzxoeafbIaYt2eurF12N0+9Gs67uOq6pcxeOzHqfWkFp8/fPXQYo86048pjNxcyIftP2AS8+91OuQJARUuEXkrLFu9zoaDW3EVz99xRvXvMGEThMoW6hsjtZZtXhVJneZzKzus4jNE0urUa249ZNb2Xt4b4Cizr7HZz3O6BWjefYfz9KtXjevw5EQUeEWkbPCku1LaPZBMw4dP8TcW+fSt0nfgA7QalGpBT/2+ZHHmj3GyGUjqTWkFlPWTAnY+rPquW+e4/lvn+eOC+6gf7P+nsUhoafCLSJh79tfvyXhwwQKxhTk21u/Ddr1y7F5Ynm25bMsvGMhZeLK0H58e7pM7MKuQ7uC0l5GXpr3Eo/Pepxudbvx9nVvawR5hFHhFpGwtu7gOq4dfS3nFDqHb2/7lmolqgW9zYZlG/LD7T/wTItnmLJmCrWG1GLcynEE8zHJ4Dun/fScp3l4xsN0qd2FD2/4kOio6KC2KbmPCreIhK1VO1fx8PKHKVmgJDO7z6R84fIhazsmOobHL3+cH/v8SKWilbhx0o10GN+BHUl/e0pxQBxPOc4dn93BgMQB3FLvFka2G6l7kEcoFW4RCUupLpXrPrqOmKgYZnSf4dllULVL12Z+r/m8cMULTNswjVpv1eLDpR+S6lID1sbvSb9zzehreP/H93ny8icZfsNwYqJjArZ+CS8q3CISlo6lHOOX/b9wwzk3eH6L0jxReXi46cMsvXMp55c6n1s/uZWL37uY+b/Nz/G6P1v3GfXfqc+83+bxQdsPeLrF0zqnHeFUuEUkLKWkpgDkqu7imiVr8s2t3zDihhFsPbiVpsOacvWoq0ncnJjl89+rd62m3bh2tBnbhlIFS7HwjoX0bNAzOIFLWMk9e7yISBYkpyYDEJXLjj+iLIpb6t9C+/Pb88YPb/DagtdoMbwFdUvX5cY6N9KpVieqFq+a7lFz0rEkZmycwQdLP+CzdZ9RMG9Bnm/5PA9e8qC6xuUkFW4RCUspznfEHWW5q3CfUDBvQR5t9ij9LurHiGUjGLF8BI/PepzHZz1O6YKlqVu6LmULlSUmKoaDxw6yce9Glv++nOTUZMrElaF/s/7cf8n9lCxQ0utNkVwmaIXbzIYBrYGdzrk6/mkvAdcDx4CfgVudc/uCFYOInL1OdJVHW+6+HCp/TH76NO5Dn8Z9+HX/r0xdP5Xvt37P+j3r+eaXb0h1qRSIKUDFohV56JKHaFm5JQkVE3LVKQDJXYK5Z3wIvAmMSDNtOtDfOZdsZi8A/YFHghiDiJylcvsRd3rOK3Ied194N3dfeLfXoUgYC9oe75ybC/xxyrSvnXPJ/pcLgNBddCkiZ5UTR9y57Ry3SLB52RdzGzAuo5lm1hvoDRAfH09iYmLAGk5KSgro+iKRchgYymP2/X7kdwCSjyUrhzmk/TDnQplDTwq3mT0OJAOjM1rGOTcUGArQuHFjl5CQELD2ExMTCeT6IpFyGBjKY/Zt3LsRvof8sfmVwxzSfphzocxhyAu3mfXAN2itpQv2jX1F5KwVLoPTRAItpIXbzK7GNxituXPuz1C2LSJnl3AcnCYSCEHb481sDPAdUMPMtphZL3yjzAsB081sqZm9E6z2ReTspsFpEqmCdsTtnLspncnvB6s9EYksOuKWSKU9XkTC0olbnuoct0QaFW4RCUsanCaRSoVbRMLSya5yfYxJhNEeLyJh6eTgNJ3jlgijPV5EwpIGp0mk0h4vImFJ57glUqlwi0hY0qhyiVQq3CISljQ4TSKV9ngRCUsanCaRSnu8iIQlDU6TSKU9XkTCkganSaRS4RaRsHRicJrOcUuk0R4vImHpRFe5jrgl0qhwi0hY0uA0iVTa40UkLGlwmkQq7fEiEpY0OE0ilQq3iIQl3YBFIlXQ9ngzG2ZmO81sZZppxc1suplt8H8vFqz2ReTsdnJUubrKJcIEc4//ELj6lGmPAjOdc9WAmf7XIiJZpq5yiVRBK9zOubnAH6dMbgsM9/88HLghWO2LyNlNg9MkUuUJcXvxzrntAM657WZWOqMFzaw30BsgPj6exMTEgAWRlJQU0PVFIuUwMJTH7Fu3ZR0Ahw8dVg5zSPthzoUyh6Eu3JnmnBsKDAVo3LixS0hICNi6ExMTCeT6IpFyGBjKY/Ytmr8IfobCcYWVwxzSfphzocxhqPuYfjezsgD+7ztD3L6InCV0AxaJVKHe4z8Fevh/7gF8EuL2ReQsoVHlEqmCeTnYGOA7oIaZbTGzXsAg4Eoz2wBc6X8tIpJlule5RKqgneN2zt2UwayWwWpTRCLHya5y3YBFIoz2eBEJSykuhSiLwsy8DkUkpFS4RSQspaSmqJtcIpIKt4iEpeTUZKKjVLgl8pz2HLeZxQKtgcuAc4DDwErgc+fcquCHJyKSvhSnI26JTBkWbjMbCFwPJALf47vmOhaoDgzyF/UHnXPLgx+miMhfpaSm6IhbItLpjrgXOucGZjDvVf/tSs8LfEgiImeW4lLIE5Vrb/4oEjQZnuN2zn0OYGadTp1nZp2cczudc4uCGZyISEY0OE0iVWYGp/XP5DQRkZBJceoql8h0unPc1wDXAuXM7PU0swoDycEOTETkdJJTk3XELRHpdCeItgGLgTb+7yccBO4PZlAiImeiI26JVBkWbufcMmCZmY12zh0PYUwiImekc9wSqTI8x21mn5nZ9RnMq2xmT5vZbcELTUQkYxpVLpHqdHv9HcADwGtmthfYBeQHKgI/AW865/RYThHxhK7jlkh1uq7yHcDDZvYb8C2+m68cBtY75/4MUXwiIunS4DSJVJm5HCwemIBvQFoZfMVbRMRTGpwmkeqMhds59wRQDXgf6AlsMLPnzKxKkGMTEcmQBqdJpMrU08Gccw7Y4f9KBooBE83sxSDGJiKSIR1xS6Q6Y+E2s/vMbDHwIjAPqOucuwtoBHTITqNmdr+ZrTKzlWY2xv/AEhGRTEtJ1ahyiUyZOeIuCbR3zrVyzk04cU23cy4V3yM/s8TMygH3AY2dc3WAaODGrK5HRCKbHuspkeqM/6465/7vNPPW5KDd/GZ2HCiA7y5tIiKZlpyarK5yiUgh72dyzm01s5eBX/GNUP/aOff1qcuZWW+gN0B8fDyJiYkBiyEpKSmg64tEymFgKI/Z98feP4iJilEOA0A5zLlQ5jDkhdvMigFtgUrAPmCCmd3snBuVdjnn3FBgKEDjxo1dQkJCwGJITEwkkOuLRMphYCiP2Rf3cxxxeeOIi4tTDnNI+2HOhTKHmRpVHmBXAJucc7v858snA5d6EIeIhDFdDiaRyovC/StwsZkVMDMDWgLZPVcuIhFK9yqXSBXywu2c+x6YCCwBVvhjGBrqOEQkvGlwmkQqT/5ddc4NAAZ40baInB10r3KJVF50lYuI5Mio5aNYvWs1tUvV9joUkZBT4RaRsLJ422Ju//R2Eiom8GTzJ70ORyTkVLhFJGzsOrSLduPaER8Xz/iO48kbndfrkERCTkMyRSQsHE85TueJndn15y7m3TaPUgVLeR2SiCdUuEUkLDw8/WESNycyst1ILih7gdfhiHhGXeUikuuNWj6Kwd8P5p8X/ZOb693sdTginlLhFpFcbcXvK+j9WW+aV2jOi1e+6HU4Ip5T4RaRXOvA0QN0GN+BorFFGdtxLDHRMV6HJOI5neMWkVzJOUevT3uxce9GZveYTZm4Ml6HJJIrqHCLSK70n+//w8TVE3npype4rMJlXocjkmuoq1xEcp15v87jX9P/xQ01b+DBSx70OhyRXEWFW0RylZ2HdtJlYhcqFKnAB20/wPcQQRE5QV3lIpJrpKSm0HVSV/Yc3sN3vb6jaGxRr0MSyXVUuEUk13hqzlPM3DST99u8T4MyDbwORyRXUuEWCRPOOdbtWcfibYv56Y+f+OPwH0RHRVM0tijVS1SnUdlGVC1eNWy7lj9b9xn/nvtvbm1wK7c1vM3rcERyLRVukVxu64GtDFk4hLGrxrJx78aT0wvnK0yqSyXpWNLJaRWKVKBz7c7c1vA2apas6UW42bJq5yq6Tu5Ko7KNeOvat7wORyRXU+EWyaV2HdrFwMSBDF0ylFSXylVVruLhSx+m6XlNqVGixsmbkRxJPsL6PeuZ/9t8pm2YxmsLXuOl+S+RUDGBgc0H0rxic4+35PT2/LmHNmPbEJc3jo9v/Jj8Mfm9DkkkV/OkcJtZUeA9oA7ggNucc995EYtIbjR+1Xj6TO3DwaMH6d2oN/+69F9UKlYp3WVj88RSL74e9eLrcWfjO9mRtIMRy0YweMFgEoYn0KJiC1656hUalm0Y2o3IhOMpx+k0oRNbD2wlsWci5QuX9zokkVzPq8vB/gN86ZyrCdQH1ngUh0iucvDoQXp+3JMuE7tQo0QNlt+1nCHXDcmwaKenTFwZHm76MD/f9zODWw1m5c6VNP5vY+774j72H9kfxOizxjnHXZ/fxezNsxl6/VAuLn+x1yGJhIWQF24zKwxcDrwP4Jw75pzbF+o4RHKbrQe20uyDZoxcPpL/u/z/+ObWb6hVqla215c/Jj/9Lu7Hur7ruLPRnbz5w5vUeLMG41aOwzkXwMiz58nZT/L+j+/zxGVP0L1+d6/DEQkbFuo/YDNrAAwFVuM72l4M9HPOHTplud5Ab4D4+PhGY8eODVgMSUlJxMXFBWx9kUg5DIwTedyYtJFHVz5KUnISA2sNpEnxJgFva93Bdby24TXWHVzHpSUu5Z/V/kmpfKUC3k5mTN46mTd+eoPrylzHg9UfzNFIeO2LOacc5lygc9iiRYvFzrnG6c50zoX0C2gMJAMX+V//B/j36X6nUaNGLpBmz54d0PVFIuUwMGbPnu0Wb1vsig4q6sq+XNb9uP3HoLZ3POW4e3neyy7/M/ld4ecLu6GLhrrU1NSgtnmqN79/0zEQ13ZMW3c85XiO16d9MeeUw5wLdA6BRS6DmujFOe4twBbn3Pf+1xOBCzyIQ8RzPyX9xJUjr6RwvsLM7zU/6DcdyROVhwcvfZDldy3ngrIX0Htqb1qOaMnPf/wc1HZPeO271+j7RV/a1GjDuI7jyBOlC1tEsirkhds5twP4zcxq+Ce1xNdtLhJR1uxaw0PLH6JATAFm95hNxaIVQ9Z21eJVmdl9Ju+2fpfF2xdT9+26vDL/FVJSU4LSXqpLpf+M/jzw9QN0OL8DEzpNIF+efEFpS+Rs59Wo8nuB0Wa2HGgAPOdRHCKe2JG0g2tGX0MUUczqPovKxSqHPIYoi6J3o96sunsVLSu35KHpD3HJ+5ewcufKgLZz4OgBbhh7A4PmDaL3Bb0Z23EseaPzBrQNkUjiSeF2zi11zjV2ztVzzt3gnNvrRRwiXkg6lsR1H13H7j9383zd56lWopqn8ZQvXJ5Pb/yUMR3GsGnfJi549wIGzB7An8f/zPG6F2xZQOOhjZm2YRpvXvMm77R+R93jIjmkx3qKhFCqS6XrpK4s3bGU8Z3GU6NQjTP/UgiYGTfWuZE196yhc+3OPD33aaq8XoXXv3+dI8lHsry+3X/upu+0vjQd1pSjKUeZ2X0m9zS5J2zvoy6Sm6hwi4TQwMSBfLb+M16/+nWurXat1+H8TckCJRnVfhRzes6hRoka9PuyH+VeLUe/L/qxdMfSM17/vXrXau7/8n4q/6cyby96mzsb3cnyO5fn+tuuioQT9VmJhMiUNVP499x/06thL+6+8G6vwzmtyytcTmLPROZsnsPbi97mncXv8PoPr3NOoXNoem5Tzi95PiULlCQmOoa9h/eyds9aFmxZwPo968kTlYfOtTvzWLPHqF26ttebInLWUeEWCYE1u9bQ/ePuNCnXhDevfTNsuoybV2xO84rN2fPnHqasncL0jdNZtG0RE1dPxPG/o+8ycWVoVLYR/S7qR7ua7ShbqKyHUYuc3VS4RYJs/5H93DDuBgrEFGBS50nE5on1OqQsK1GgBLdfcDu3X3A7AMdSjrH/yH6Opx6nWGwxPdFLJIRUuEWCKNWlcsuUW9i4dyOzus86a55+lTc6L6UKenO7VJFIp8ItEkQvfPvCycFol1W4zOtwROQsoFHlIkEye9Nsnpj9BDfWuZG+Tfp6HY6InCVUuEWCYNvBbdw46Uaql6jOf6//b9gMRhOR3E9d5SIBdjzlOF0mdiHpWBKze8wmLq8elygigaPCLRJgj818jG9//ZbR7UdTq1Qtr8MRkbOMuspFAmjKmim8/N3L3N34brrW7ep1OCJyFlLhFgmQn/74iZ6f9OTCcy7k1Vaveh2OiJylVLhFAuDw8cN0HN+RaIvWs6ZFJKh0jltCYsOeDXz181f8uP1Hftn/C/uO7CNfnnwUz1+cmiVq0qRcE1pWbknx/MW9DjXLnHPc/tntLPt9GZ93/ZwKRSt4HZKInMVUuCVojiYfZfSK0bzxwxss3bEUgFIFSlG1eFVKFyzN8dTj/LLvF77++WuOpRwjyqK4puo19GrYi9bVWxMTHePtBmTSi/Ne5KMVH/FMi2dy5RO/ROTs4lnhNrNoYBGw1TnX2qs4JPCcc0xYPYFHZjzC5n2bqRdfj8GtBtOmRhsqFq34t2uaj6ccZ+G2hXy27jOGLxvO5xs+p0KRCjxx+RP0qN8jVxfwqeun0n9mf7rU7sJjlz3mdTgiEgG8PMfdD1jjYfsSBDuSdtBmbBu6TOxC4XyF+bLblyzts5R+F/ejUrFK6d6IJCY6hkvPvZTnr3ieX+//lU9u/IT4uHju+OwOar5Vk0/XferBlpzZ6l2r6TqpKw3LNmRY22G6yYqIhIQnhdvMygPXAe950b4Ex3e/fUeDdxowY+MMXmv1Gkt6L6FV1VZZKmh5ovLQpkYbFvRawNSbphKbJ5a2Y9vSZkwbNu3dFMTos2brga1cM/oaCsQU4OMuH1MgpoDXIYlIhDDn3JmXCnSjZhOB54FCwEPpdZWbWW+gN0B8fHyjsWPHBqz9pKQk4uJ0N6ucODWHX+74klfXv0qpfKV4ps4zVCpYKSDtJKcmM3HrRIZvHk4qqfSs0JPO53Ym2qIDsv7sSEpO4r6l97HjyA4G1x9M9ULVs78u7Ys5phzmnHKYc4HOYYsWLRY75xqnO9M5F9IvoDUwxP9zAjD1TL/TqFEjF0izZ88O6PoiUdocPjf3OcdAXMvhLd2eP/cEpb1f9/3q2o5p6xiIu+DdC9zS7UuD0s6ZJB1NcpcNu8zFPB3jpv88Pcfr076Yc8phzimHORfoHAKLXAY10Yuu8qZAGzPbDIwF/mFmozyIQ3LIOcfAxIE8NusxutXtxhfdvgja5VznFjmXKV2mML7jeLYc2ELj/zbmiVlPcCT5SFDaS8/Bowe5ZvQ1zPttHiPbjeSKyleErG0RkRNCXridc/2dc+WdcxWBG4FZzrmbQx2H5NyAxAE8Necpbm1wK8NvGB700d9mRqfanVh992q61u3Ks988S8N3GzL/t/lBbRdg/5H9tBrVivm/zeej9h/RpU6XoLcpIpIe3TlNsmXSlkn8e+6/6dWwF++1eY/oqNCdcy5RoATDbxjOF92+4M/jf9JsWDP6fdGPpGNJQWnvpz9+4pL3L2HRtkVM6DRBRVtEPOVp4XbOJTpdwx12xq4cy1s/v0W7mu14t/W7RJk3u9HVVa9m5V0ruefCe3j9h9epM6QOX//8dUDbmLVpFhe9dxG/H/qdr27+inbntwvo+kVEskpH3JIl83+bT/cp3albpC4fdfgopEfa6SmUrxBvXPsG39z6DbF5Ymk1qhU9P+7JlgNbcrTeI8lHeHj6w1wx4grKxpVl4R0LaVGpRYCiFhHJPhVuybQdSTvoOL4j5xU5j2dqP0NsnlivQzqp2XnNWHrnUvo3689HKz6iyutVuHfavfyy75csrSfVpTJh1QRqvVWLl+a/xB0X3MF3vb6jcrHKQYpcRCRrVLglU46nHKfzhM7sO7KPyV0mUyimkNch/U1snliea/kcG+7dQI/6PXhn8TtU+k8lrhp5FaOWj2LnoZ0Z/u7mfZt57bvXOP+t8+k8sTMF8xZk+i3Teff6dymUL/dtq4hELj1kRDLlkRmP8M2v3zCq3SjqxdcjcU2i1yFlqELRCgy9fihPXP4EH/z4AcOWDuOWKbcAUKVYFWqUrEGx2GIA7P5zN6t2rTrZtd6kXBPGdBhDp1qdPD8NICKSHhVuOaOxK8fy2oLXuLfJvXSr183rcDLtvCLnMSBhAE82f5JF2xYxY+MMlv2+jA17NrB291qccxTPX5zLK1zOhedcyPXVr6dK8Spehy0icloq3HJaK3eupNenvWh6blNevuplr8PJliiLokm5JjQp18TrUEREckznuCVD+4/sp/249hTOV5jxncaTNzqv1yGJiEQ8HXFLulJdKj0+7sGmfZuY1X0W5xQ6x+uQREQEFW7JwKBvB/HJuk8Y3Gowl1W4zOtwRETET13l8jdf//w1T8x6gpvq3MR9F93ndTgiIpKGCrf8xeZ9m7lp0k3ULl2b/17/X8zM65BERCQNFW456UjyETqO70hyajKTO0+mYN6CXockIiKn0DluAXzP1r7n83tYvH0xn974KdVKVPM6JBERSYeOuAWA/y75L8OWDuOJy57g+hrXex2OiIhkQEfcWeScI8WlYNhZc0vM+b/Np++0vrSq0oqBCQO9DkdERE5DhfsMth3cxqTVk5i1eRYrfl/BL/t/ITk1mSiLIr5gPOeXOp9Lyl9C2xptaXxO47AbzLXt4DY6jO/AeUXOyxWP6RQRkdNT4c7Awq0LeeabZ5i6fiqpLpVKRSvRpFwTOtbqSMGYghxLOcbWg1tZ/vtyBn07iGe/eZbKxSrTo34P7rnwHkoUKOH1JpzR0eSjtB/XnoNHDzL9lukUz1/c65BEROQMQl64zexcYARQBkgFhjrn/hPqODKy/eB2/vnVPxm/ajzF8xfnkaaP0LNBT6qXqJ7h7+w9vJeP137MmJVjGJA4gBfnvUifRn3of1l/ShYoGcLoM885x92f3833W79nUudJ1Cldx+uQREQkE7wYnJYMPOicOx+4GLjHzGp5EMffTFw9kfPfOp9P1n7CgOYD2NRvE8+1fO60RRugWP5i3NrwVr6+5WtW3LWC9ue35z/f/4dqb1Tjte9e41jKsRBtQea9MO+Fk4PR2p/f3utwREQkk0JeuJ1z251zS/w/HwTWAOVCHUdax1OO88BXD9BpQidqlqzJyrtXMjBhIIXzFc7yuuqUrsOIdiNYducympRrwgNfP0CjoY1YuHVhECLPno9WfET/mf25qc5NPNXiKa/DERGRLDDnnHeNm1UE5gJ1nHMHTpnXG+gNEB8f32js2LEBazcpKYm4uDgA/kz+kydXPcmSfUtoV64dd1W+i5iomIC045xj/p75DN4wmD+O/UGn8p3oWbEnsdGxAVl/dizeu5j+K/pTq3AtXqz3InmjsvfEr7Q5lOxTHnNOOcw55TDnAp3DFi1aLHbONU53pnPOky8gDlgMtD/Tso0aNXKBNHv2bOecc7sP7XZN/tvERT8V7T788cOAtpHWvsP7XO9PezsG4qq+XtXN2TwnaG2dTuKmRJf/mfyuzpA67o8//8jRuk7kUHJGecw55TDnlMOcC3QOgUUug5royQ1YzCwGmASMds5N9iKGnYd20vzD5izbsYwpXabQo0GPoLVVJLYI717/LrO6zyLVpdL8w+b0ndaXpGNJQWvzVPN+ncd1H11HxaIVmdl9JsXyFwtZ2yIiEjghL9zmu9D5fWCNc+7VULcPkJScRKtRrdi4dyNf3vxlyO4U1qJSC5bfuZx+F/VjyMIh1H27LjM3zgx6u1PXT+WqUVdRrnA5ZnafSemCpYPepoiIBIcXR9xNgVuAf5jZUv/XtaFq/NCxQ/Rf0Z9VO1cxpcsUEiomhKppAArmLcjgqwfzza3fkDc6L1eMvII+n/XhwNEDZ/7lbHhn0Tu0HduWWqVqMbfnXMoWKhuUdkREJDS8GFX+rXPOnHP1nHMN/F/TQtV+/5n9WX1gNR91+IhWVVuFqtm/aXpeU5b2WcpDlzzEez++R+0htZm4euKJ8/85dvj4Yfp81oe7Pr+La6tdS2KPROLj4gOybhER8U7EPWRkYMJAnqr9FB1rdfQ6FPLH5Oelq15i/m3zKRZbjE4TOpEwPCHHl459++u3NBraiKFLhvJI00eY0mWKHtEpInKWiLjCXTx/cZqVbOZ1GH9xUfmLWNJnCe9c9w6rd62myXtNuHLklczYOINUl5rp9azdvZauk7py2QeXcej4Ib66+SsGXTGIPFG6s62IyNlCn+i5RJ6oPPRp3Ieb6t7Eu4ve5dUFr3LlyCspX7g8nWt15tpq13JhuQv/clOYVJfKxr0bmb1pNmNXjWXWplkUiClA/2b9efyyx3WULSJyFlLhzmUK5yvMv5r+i3svupdJqycxbtU43vjhDV5d4BuAXzx/cYrFFiPFpbD94HaOphwFoFLRSjyV8BR3Nr5To8ZFRM5iKty5VGyeWLrV60a3et3Yf2Q/C7YsYMn2Jfx24Df2H91PtEVTJq4MVYtX5fIKl1OjRI2we6SoiIhknQp3GCgSW4RWVVt5OgpeRERyh4gbnCYiIhLOVLhFRETCiAq3iIhIGFHhFhERCSMq3CIiImFEhVtERCSMqHCLiIiEERVuERGRMGKBeoxkMJnZLuCXAK6yJLA7gOuLRMphYCiPOacc5pxymHOBzmEF51yp9GaEReEONDNb5Jxr7HUc4Uw5DAzlMeeUw5xTDnMulDlUV7mIiEgYUeEWEREJI5FauId6HcBZQDkMDOUx55TDnFMOcy5kOYzIc9wiIiLhKlKPuEVERMKSCreIiEgYibjCbWZXm9k6M/vJzB71Op7cysyGmdlOM1uZZlpxM5tuZhv834ulmdffn9N1ZtbKm6hzFzM718xmm9kaM1tlZv3805XHTDKzWDP7wcyW+XP4lH+6cphFZhZtZj+a2VT/a+UwC8xss5mtMLOlZrbIP82THEZU4TazaOAt4BqgFnCTmdXyNqpc60Pg6lOmPQrMdM5VA2b6X+PP4Y1Abf/vDPHnOtIlAw86584HLgbu8edKecy8o8A/nHP1gQbA1WZ2McphdvQD1qR5rRxmXQvnXIM012t7ksOIKtxAE+An59xG59wxYCzQ1uOYciXn3Fzgj1MmtwWG+38eDtyQZvpY59xR59wm4Cd8uY5ozrntzrkl/p8P4vvQLIfymGnOJ8n/Msb/5VAOs8TMygPXAe+lmawc5pwnOYy0wl0O+C3N6y3+aZI58c657eArSkBp/3Tl9QzMrCLQEPge5TFL/F28S4GdwHTnnHKYdYOBh4HUNNOUw6xxwNdmttjMevuneZLDPIFaUZiwdKbpericU15Pw8zigEnAP51zB8zSS5dv0XSmRXwenXMpQAMzKwpMMbM6p1lcOTyFmbUGdjrnFptZQmZ+JZ1pEZ1Dv6bOuW1mVhqYbmZrT7NsUHMYaUfcW4Bz07wuD2zzKJZw9LuZlQXwf9/pn668ZsDMYvAV7dHOucn+ycpjNjjn9gGJ+M4ZKoeZ1xRoY2ab8Z0e/IeZjUI5zBLn3Db/953AFHxd357kMNIK90KgmplVMrO8+AYPfOpxTOHkU6CH/+cewCdppt9oZvnMrBJQDfjBg/hyFfMdWr8PrHHOvZpmlvKYSWZWyn+kjZnlB64A1qIcZppzrr9zrrxzriK+z7xZzrmbUQ4zzcwKmlmhEz8DVwEr8SiHEdVV7pxLNrO+wFdANDDMObfK47ByJTMbAyQAJc1sCzAAGASMN7NewK9AJwDn3CozGw+sxjeS+h5/92akawrcAqzwn6MFeAzlMSvKAsP9I3KjgPHOualm9h3KYU5pP8y8eHynacBXNz9yzn1pZgvxIIe65amIiEgYibSuchERkbCmwi0iIhJGVLhFRETCiAq3iIhIGFHhFhERCSMq3CIRxMxK+J9utNTMdpjZVv/PSWY2xOv4ROTMdDmYSIQys4FAknPuZa9jEZHM0xG3iGBmCWme0zzQzIab2df+ZxC3N7MX/c8i/tJ/G1fMrJGZzfE/dOGrE7d+FJHgUuEWkfRUwfcYyLbAKGC2c64ucBi4zl+83wA6OucaAcOAZ70KViSSRNQtT0Uk075wzh03sxX4bg/8pX/6CqAiUAOog+8pSfiX2e5BnCIRR4VbRNJzFMA5l2pmx93/BsOk4vvcMGCVc+4SrwIUiVTqKheR7FgHlDKzS8D3+FIzq+1xTCIRQYVbRLLMOXcM6Ai8YGbLgKXApZ4GJRIhdDmYiIhIGNERt4iISBhR4RYREQkjKtwiIiJhRIVbREQkjKhwi4iIhBEVbhERkTCiwi0iIhJG/h88xirGSIoS9wAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 576x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "np.random.seed(0)  # For reproducibility\n",
-    "\n",
-    "# create your code here:\n",
-    "# create an array with 501 timesteps (in terms of day, so day 0, day 1,...,day 500)\n",
-    "time = np.arange(501) \n",
-    "# m is the length of tim\\epsilon_steps (so number of time series observations)\n",
-    "m = len(time)\n",
-    "# give an intercept of 1 mm\n",
-    "y_0 = 1 \n",
-    "# provide a particular rate of 0.02 mm/day\n",
-    "r = 0.02 \n",
-    "# make the time series observations (so far without noise) based on the above two components:\n",
-    "y1 = y_0 + r*time \n",
-    "\n",
-    "# plot y1 versus time:\n",
-    "plt.figure(figsize=(8,4))\n",
-    "plt.grid()\n",
-    "plt.plot(time, y1, color='red')\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('Time (day)')\n",
-    "plt.title('$$Y$(t) = 1 + 0.02 t $')\n",
-    "\n",
-    "# introduce (add) a seasonality to the generated data\n",
-    "# A sine signal Am*sin(omega * time + phi_0) can be added\n",
-    "# omega=2*pi*f can be obtained from f = 0.01 cycle/day (1 cycle per 100 days)\n",
-    "omega = 2 * np.pi/100 \n",
-    "# the amplitude of signal is assumed to be 1 mm \n",
-    "Am = 1 \n",
-    "# the initial phase is assumed to be 0.2π (radian) \n",
-    "phi_0 = 0.2*np.pi # initial phase\n",
-    "# add the sesonality to y1 to make y2\n",
-    "y2 = y1 + Am*np.sin(omega * time + phi_0) \n",
-    "\n",
-    "# plot y2 versus time:\n",
-    "plt.figure(figsize=(8,4))\n",
-    "plt.grid()\n",
-    "plt.plot(time, y2, color='blue')\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('Time (day)')\n",
-    "plt.title('$$Y$(t) = 1 + 0.02 t + sin(0.02πt + 0.2π) $')\n",
-    "\n",
-    "# add an offset to y2 at epoch (time instance) 300\n",
-    "t_k = 300 \n",
-    "# offset size of your choice (for example 5 mm) - the jump in your data!\n",
-    "O_k = 5 \n",
-    "y3 = y2.copy() \n",
-    "y3[t_k:] = y3[t_k:] + O_k\n",
-    "# plot y3 versus time to see the effect of the offset:\n",
-    "plt.figure(figsize=(8,4))\n",
-    "plt.grid()\n",
-    "plt.plot(time, y3, color='g')\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('Time')\n",
-    "plt.title('$$Y$(t) = 1 + 0.02 t + sin(0.02πt + 0.2π) + 5 u_{300}(t)$')\n",
-    "\n",
-    "# add randon error (white noise) which follows a normal distribution \n",
-    "# (mean of zero mm, and standard deviations of 0.5 mm)\n",
-    "# change these parameters to see the effect\n",
-    "mean = 0 \n",
-    "sigma = 0.5 \n",
-    "et = np.random.normal(loc = mean, scale = sigma, size = m) \n",
-    "y4 = y3 + et \n",
-    "\n",
-    "# plotting:\n",
-    "plt.figure(figsize=(8,4))\n",
-    "plt.grid()\n",
-    "plt.plot(time, y4, color='red')\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('Time')\n",
-    "plt.title('$$Y$(t) = 1 + 0.02 t + sin(0.02πt + 0.2π) + 5 u_{300}(t) + N(0,0.5^2)$')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d1a918b4",
-   "metadata": {},
-   "source": [
-    "### Exercise 2. Stationary time series (Video 2)\n",
-    "**Introduction:** In the previous exercise, we created and plotted the $Y_4$ time series, now we check its stationarity. Remember that we need to ensure *stationarity* of the time series data-set for *forecasting and predictive models*. \n",
-    "In this excercise, you can test the stationarity of the time series using transformation and visual inspection and the Augmented Dickey-Fuller (ADF) test (The ADF test is optional). \n",
-    "\n",
-    "**Background knowledge:** The ADF test can be performed by using two hypotheses (Null Hypothesis and Alternative Hypothesis):\n",
-    "\n",
-    "1. Null Hypothesis $H_o$: we assume that the time series is not stationary. \n",
-    "2. Althernative Hypothesis $H_a$: we assume that the time series is stationary. \n",
-    "\n",
-    "If the test statistic is smaller than the critical value, the null hypothesis is rejected and therefore the time series is stationary. In this case the the p-value becomes very small. In python, there is a package: **statsmodels** which has the function of **adfuller method**. We use the adfuller() function to test the stationarity of the data-set. Regarding the interpretation of the adfuller function, the first output is the test-statistic, the second one is the p-value, etc.\n",
-    "\n",
-    "**Excercise:** We take the time series and the noise from the Excercise 1 $Y_2$, $Y_4$ and $\\epsilon_t$. We also use the single differencing method to make the time series stationary and plot the results. Later we will also use the least squares method (best linear unbiased estimation - BLUE) to de-trend the data. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ede822f5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from statsmodels.tsa.stattools import adfuller # optional"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "d775ad7c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test statistics:-15.24, pvalue:0.0000, Critical_value(1%):-3.44\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# we apply single differencing to y2 and y4 to see if they become stationary.\n",
-    "diff_y2 = np.diff(y2)\n",
-    "# insert the first entry/element as zero\n",
-    "diff_y2 = np.insert(diff_y2, 0, 0)\n",
-    "# plotting:\n",
-    "plt.figure(figsize=(8,4))\n",
-    "plt.grid()\n",
-    "plt.plot(time, diff_y2)\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('Time')\n",
-    "plt.title('Single Differencing')\n",
-    "# it looks non-stationary. It looks like a cos function, can you explain this?\n",
-    "# how to make y4 stationay? (different methods exist, we now use single differencing, later the BLUE fit)\n",
-    "# single differencing\n",
-    "diff_y4 = np.diff(y4)\n",
-    "diff_y4 = np.insert(diff_y4, 0, 0)\n",
-    "# plotting:\n",
-    "plt.figure(figsize=(8,4))\n",
-    "plt.grid()\n",
-    "plt.plot(time, diff_y4)\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('Time')\n",
-    "plt.title('Single Differencing')\n",
-    "# it looks stationary, but we show it using ADF test\n",
-    "\n",
-    "# OPTIONAL\n",
-    "# Stationary test using ADF test (this is optional)\n",
-    "# show that the single differencing gave stationary dataset:\n",
-    "test_diff_y4 = adfuller(diff_y4)\n",
-    "test_statistic = test_diff_y4[0]\n",
-    "p_value = test_diff_y4[1]\n",
-    "critical_value = test_diff_y4[4]\n",
-    "print(f'Test statistics:{test_statistic:.2f}, pvalue:{p_value:.4f}, Critical_value(1%):{critical_value[\"1%\"]:.2f}')\n",
-    "# Test statistic < Critical value and p_value is small so Null hypothesis is rejected => Time series is Stationary\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9aea5c77",
-   "metadata": {},
-   "source": [
-    "### Exercise 3.  Autocovariance function and PSD (Video 3)\n",
-    "**Introduction:** In this exercise, you will focus on normalized auto-covariance function (ACF) and the power spectral density (PSD), and the auto-regressive moving average (ARMA).\n",
-    "\n",
-    "**Background knowledge:** In python there are functions for ACF and PSD. These functions are given from statsmodels.graphics.tsaplots (plot_acf). These functions create automatically a plot. Regarding the PSD, there is a function from the package of scipy (signal) and you need to use the signal.periodogram to calculate the PSD. These libraries are already imported in this notebook. Alternative way to compute the PSD is based on the least-squares harmonic estimation (LS-HE), which is based on hypothesis testing (see optional materials on relation between FFT PSD and LS-HE PSD).\n",
-    "\n",
-    "**Exercise:** We use the above functions to plot the ACF and PSD of white noise time series. Later we also compute them for the ARMA(p,q) process. We generate a white noise process, similar to that created in exercise 1 ($m=501$). We will see that white noise does not show any temporal correlation. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "89e81bde",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0, 'Frequency')"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# ACF + PSD of a white noise process\n",
-    "mean1 = 0 \n",
-    "sigma1 = 1\n",
-    "m = 501\n",
-    "time = np.arange(m) \n",
-    "Fs = 1 # sampling rate\n",
-    "\n",
-    "# simulate a normal white noise process with the given mean and standard deviation\n",
-    "e1 = np.random.normal(loc = mean1, scale = sigma1, size = m) \n",
-    "yt1 = e1\n",
-    "\n",
-    "# plot the time series\n",
-    "plt.figure()\n",
-    "plt.grid()\n",
-    "plt.plot(time, yt1, color='pink')\n",
-    "plt.title('White noise time series')\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('time')\n",
-    "\n",
-    "# plot the normalized auto-covariance function (ACF) of the generated noise.\n",
-    "# explain the plot of ACF (do you see temoral correlation in this time series?)\n",
-    "ACF = plot_acf(yt1, lags=None, alpha=0.05, title='ACF of white noise', color='pink')\n",
-    "plt.grid()\n",
-    "\n",
-    "# plot the white noise PSD\n",
-    "F, PSD = signal.periodogram(yt1, fs=Fs, scaling='density', return_onesided=False)\n",
-    "# F, PSD = signal.periodogram(yt, fs=Fs, scaling='density')\n",
-    "plt.figure()\n",
-    "plt.grid()\n",
-    "plt.plot(F, PSD, color='pink')\n",
-    "plt.title('PSD of e1')\n",
-    "plt.ylabel('Power: PSD')\n",
-    "plt.xlabel('Frequency')\n",
-    "# The PSD values seem to be the same at all frequencies (no frequency dependent).so it looks flat indicating that all \n",
-    "# frequencies have identical contributions to construct data (variations).  \n",
-    "# Think of the white LIGHT that has similar characteristics."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "498f79f0",
-   "metadata": {},
-   "source": [
-    "### Exercise 4.  ARMA: MA(1), ACF + PSD (Video 4)\n",
-    "**Introduction:** In this exercise, you will focus on special case of ARMA(p,q) process, namely ARMA(0,1)=MA(1) process. You then you compare the ACF and PSD of the genearted time series.\n",
-    "\n",
-    "**Exercise:** You are asked to generate a MA(1) time series. As you know from the lectures/videos an MA(1) is of the form\n",
-    "\n",
-    "$$\n",
-    "Y_t = \\theta \\epsilon_{t-1}+ \\epsilon_{t}\n",
-    "$$\n",
-    "\n",
-    "You may assume $\\theta=0.8$, and the time series is assumed to be stationary, so $\\mathbb{E}(Y_t)=0$ and $\\mathbb{D}(Y_t)=\\sigma^2$, with $\\sigma=1$. For generating the time series, you need an initialization of one sample generated randomly as $(0,\\sigma^2)$, and then use the above recursive formulae. The variance of $\\epsilon_t$ is obtained from\n",
-    "\n",
-    "$$\n",
-    "\\sigma_{\\epsilon}^2 = \\frac{\\sigma^2}{1+\\theta^2}\n",
-    "$$\n",
-    "\n",
-    "You can then apply the ACF and PSD to the generated MA(1) noise process."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "99ad0a76",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0, 'Frequency')"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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Xxd81SsyWNMNtnb5ObLyMGI8YwEBfsXTkifr3ySgBPEhRk1SbYADt9hhLy5ZbZAC1Ys9WDiqgvFA3As9evP9c0MlzrFMCMNgAmLEIpc6RtADhIQJ0VuTMvEDIt17NxNtp907dtAGi8MKmMKOcYRBsAHMIl68L1YaEbR9AJ3rcFEFTPb6PpbyrN8TZCa40Ktgw8m7exdM0mI9fuY6ZGeAbbwhbRhaYJMM6yR3SH7Ii1vY5GoGbibdT9J6RViWAqsoAggQw+5icinNhHzCLA0uOvNe457MPoFNQxGAx6fXVtnrvfOPgHNOqbmQMePN4Y8DUDBKAVLfo8ZjywP1RQe/Fa829P592X2vzf+x7vXWi+b0ZTUkA7VYBpSyWTAuRLKgGFVCxePs94NzlbO9IsV6F1QYwCwO/Wk13M5yaTunAbUa9XZTybIzXZAM4fVFMwtLdzqVqa4e6ZTJqX8eJTU40a3e5fQ8YHW+uzLZBtQEoHGBqGrg3Apw421y21Q5kACpMEopJFZkFQQIoGBNNSABjhgFZ9ZEA2vSB3zklfOxtmKmK52ciV0S9o9VqwKnzQhpqF+o2Eo0uF0zSQinqsnUJgMzp88ZU1Da9lWzvmSaoLHS+c0rEyekkyVJf9NZ/t3g4S0sSQHNFpudvKMv0vGkVULHftbu9gGq1RlyTLJAqhpLCP712ArcJ96KD3as1oBzRVK2i7k8habt2U9CsqjEOHQMWDIhV5uQ0sGtLPjTNzAAPlE1fNcNEOG05SFymUVdcdQbgmmRaXI25ICWAcpNrplZXdw/GgMWdcti9wnzz3AjW6UZgZ59rstPNVIXnU7VaiGqwuxmAXIFm7Tzqap9ZfHyfYHDt+sClqPypKWCgX9w7dQE70N+gU9Jt0mHXVQw5Enj8bNxgamKQMykMgA0MoP7bRWs0GKdnhFpiwYAXyU5IFVvWvmJiZs1MVp3iQADEu4nmhdUSsjIA5tl1A3WpUFtRAfVEDCDEApplyBVoVu8D0yq2pjGFetrYi1kp9INUS0wqdoCpafTKXuldvxyXzrre2kRDqgpIZQDaQJc2AJcK6I133WGGxybETl0fsMXG44tWbUEdxQDUSa9gL6A83pWYmnbvVTDaAORFCyqgelC95rJoBV0uAUSTd7OrOkAwgLffi094UioQP5T7TVGZjt5eYctQdfjMjS7pu7LyEWOv3RQddsVSdzp956vJS8qkFlJ/myQA04pahyx6IsWm8fpR8ff5ve50QPOqBuN7HTSZtwRKegEByerNzABTKdIeIAz8tuNU095tpU0fjAr72NiEGM+x/pAiueUiAchpOEgAswubCujmXbdXjc4A9NXubLv89UUSgEYzZZUAfDrxyXPAsTMeeWmZyTaJbXyxMCbTpFmt2Z8lXtRv5/gRMuflMAJnyapTJQDVC8i2g/vw8QazteUFAFeux71ifOkwFJkJ750XdrRpw4LQVwXULKrVQiWAwAAAbaKpAsdOA0dP2d8zTUwqYhPuLKz8pDFaZQDMjY/rrVvNUQWkMwBJg8oATHYTZhgnzYQE4FIBaWVnmVRsaFbXLJNb+0TG8jsKmkHeRuKEwW1aQq9XFq+YVu0qtndtebnKaLb4mVp+J6s1ge5mACaOPx1NFpOeEkDNMLnE4nvAfN0OaCuX7CqgHGmxqYBUw69N1eOabH0mYr0eebra2aSp0XHz/hAJ46oyQ4fopPlflwCyqsdMth1pqM8ykecVRyfBhCy++a59AK24vkrPsm6TAIjot4noOhFZ5MM2w2QDkPd6HEGp1I6QKgHMImITvWoD8FUB5ckB9FW4wYhqsgFUa5ZVM+JpjfYDSz3TGIDP4E0b6IeOiYNBki8a3ktZMZsJyJK4vZCkyHDQeh1VUtOkL/mOvr/DB+2SAGZsqknXWG9SqmNW3Mm7TwL4HQCfKKz0ugpIuSelApe/N3MjaqGpg9tWJlk66f3RRiTNNBhdDdWQMxklgImpdFrTpAqTBKBHxGRNrQNEDMBDAjAxAJsKKI1WNY/xSfeGOBNNzvSG97KumNV3OgIqMeSmzWSIN03ccrxlcQW1SdpZYZIA7o34qYZYs001U26pSyUAZv46gNuFEWCyAUgJwBWWlrkhIairy/qHzOFLvnlcxNLPgoQE0IQb6PgE8K2302Pe2DZx1bMySAA6U03z93eqgBxumQkVUMoqVP2Gh46Ks4YTg1/SZyhPbsQzLhpMzLkJVUlncQAB/UAYkxHY1PYmtUq5ifOFW7Wr2F59MCpiGp25mL6As3my+aB+tGZx0zBxwcYlItoA4CvMvMvy/AUALwDA8PDwngMHDjRVzsjICIaG4jspH0c/llMPRriKQxCePKvQg+3Ujxs8g2MwBxXbiX4MgjCIEi5gGutJxIeZ5Br6qIQ3eAz3IT7uelSwkcTK9wRP4BpSJs4I+0jQepDT/dR3oA8PUQU3eQZHI5r3YAD9IHwTY/U6peEKT+MuqtjhqL+k6zUewxjsqzXZthL3uAoCsBAlUMQcLvAU3scU+kH4MC0AALzBY1iDCoapgilmvIzRWLnv8gSuYwbb0IfVVMF9ruKN6NttQi/WUS/O8CQuYrr+zhEexx0kJyL5/GUexVQ0C8h7VWYcxwRuRu89hQEspnKsjSVWowfbqB9jXMNriB95uRcDGKIy7nEVb0Z0bkYvHqFefMDTOI643WABSliCMi5jGgTgea3eJpj6djuxEmXspAG8zmPYhj5UwXgbE1iIEvbQIGaY8VL03ZagjCcpvhHvJR6p12QQhGdpAa7xNB6mCg7zGB44+pXEyMgIVg8tEtFfgVj7urAEZUyihnFl1n8Og+ijxiT8Pk9iE/XhAVdxDlN4PKLf1I92oB8PUQ8u8hTOIFsolQoIH6UFOMOT2Ex9eJ8ncQF222Mr33n//v2HmTnh79zxDEDF3r17+dChQ02Vc/DgQezbty9+883jQtUy2A88ExV/4apYeQ8vBx7daM7s6GlgchIYnwKGlwFXogOhB/uFL/Hu7cCSheLeuSvA+Sviett6YNVKP4JfjOrp46P+7hngxh1g6SLgiW3i3qFjqI6MobzvGRG6+vSF9HxWrxSbys5dAdYOA5sfsdOl1hFoGG/lSu6dU/GdwAsGhNT0YKyx8lnzELBlnZA6XovMQLu3ifa8cUek/+hTIt+vHxbPH90ovs2Js8AHt4ChwcYJU2cuiiMnN60FHnm4QevOzeZ9C/L5s7saO6hf1PqXbP83jouV4bJFwOPb4mkufyCC1Q30Ac8+Hn926JgwEC8cBDasEd/ozCXxzsplwI5NZpqe3yva6RtvxOttgLFvp2FkDHj3fWDdw0KFtX61/7vXbwPH3wf27hQulCUS/eHeiFg5l0rAx54WaW/dFeNFxXO7G5sXR8aAw++KcXH1BvDkdvEt5Le990C0m6nOTz4tIvKWCFgwCDz9WDrtpnH18ltxiXb9KuD8VRH4b8s64RUIiDApy5fE8zt6WtRR9uUsmJgS0vaWdWJ8blgjyragqe8cgYiMDKBoG0CxMKmAZEdwGUS5Jp6XS3G3RrmhQ1PFtAQvAyUM5aI5N1DpyaKqwK7eSMbB18M4vPo28NKbCk0a3bWamBhi8ZMM6pEaJ3XkJjE8i+itqniq1eQOZC8vIaVcZjFg5QH3LjdI+ezBmGCK128rCVNob9Z+5IPzVwXjPXlOMPw0lZ4JpnDQOkx9z1SvsmIEfvd94P1LYoPleYcdTPaBUjlnI7D0BJwCpqft6QBzkENA0G50ClDL1VVA3WcELhZGG4BHfKAaNxiAOnDUTizRqprSq2PLySmuPycZqCuLG6jcS6DH6n9H2xehk6VPIKbYPURAWfXbN9kAuJG50YCq2QBcm4EkE1cn+FeOAN98M57Op31UWsYnhVT17vtauR7famLKzwgsPUTahYoWBGB0zJxOx+QUcEpKkwkOkEyf5iVnYgDTmhrE1g6qDj2LJ1fafbUvv3fe/b7J7gGIMxFcbsGAwsC61AhMRH8I4BUA24noEhH92KwVzmyOBVQ1TCyJdxFNZuX4arL+IW0TiucXzhwUS75nmAyZs7mB6mGtravQlDz1MmuR1JQWQbVWS06QJtdRHzdQyQBqVRHCm9kyIWVob+bGN6/7b6dM5PEbDTpdTcgcf543M9AZwIjnmQN37jekv/r8r9dH+546ZPqrN4E3T4jruhG4lvxGNiN+3YDsOY3ZvnN9X0PUX9KCFJpoaMUIXPeYm30OUGgsIGb+gcIKt204Mq0sdTCLj1YuxXffyg9p8/jw/b5TKWKnDSbVE+v3HVDTyjrMWDbFpJFl86JRGYBNxaPfN0oAHgNPjqv7o0LvvsGi5/ZRAanMSN8r4lIBmfLxkRh0CSDvuUFnAGmrVYkYY5fhoKOfpnYwta1MJ21jQJyZ6v11phafqUbGsAG9cQnA5xva0kh6pcQ8ncJwYu+24AYq1ardKgEUCjmx6fHM1VXp9Vv2FUxdBWSQAFo9vCPzDkeTCkh539sNlJMSgJUZpYjTJh9qgtkGkJAAtAlfZ6hqGpvUo/6WsZruWyI9Ztl8xNxoE2nzSZMW9fe9JADDeyNj7rAKWaDauCo9DqlVp0NJ19hoEv0xrZAdDEDdFzIYeQrVDP1VlwBOnsMG6m1817KnDSDtBDr5Vy2vbFiwxN51PHPh7gNhewGSe2ZmEd0bDVR+sFIpPthk57t5V/x7ZFx4lejvSnWGKi5SigTgy+KzviLT1FhMECPj8U7tLQEoadMYgI2uWk0MSL1MrgGgeGe32gA0mtTn718S/xYvTL5rsx3ISdPWDq72qVZFJEuV1joD0IJ4GScB7d4FZX9FVhvA4XfFXx/PsDSoeZdK/kwwdri9LgEY0rskgIF+MRECQH9vI3/9e+gMoK9XMMN7Dxr0ezkDeO5KrhoWUvK5HPutIjZvlPI9WCcDulcCqKskdAlA+wimFZfsBHo/qKuAWowF1Kz3R60m3A6PKW53mRgAkiEb5GSnDzIbXXKVlZjIIdpr01oxgAf7zR4UpvMUTKvTqstYz/EJ1KTOAhSdb1W4j5ra6e1TcW+OGjdsR/rpZK4VYlYkVEB5Tw7aRO6bv95GsXctEl0ij+i5XF1XeuL2M50WnYkMRi678sS5csmvnX1jQtm8+KZmhDvyB7eUx45v74IqCZcMc8ksoYsZgCoBqAPN4L1ierckLWAKjGeiNjFwsw58deWiG7FNK0lXProEMK2sdn1WiTYjumSai4aADz8BVCpmNU7NMPGZyjUF8qu/Y7mvG/fk97p0TewhuHI9+Y48MEZd5dc9pbTBb2wfR5vduS+Yi223rL76zBMyu6cfy8gATBKAYxKs1ZJxtfT0e3eapWeJtJW7rxeQzKeUMu3ZvPikzeK6IXiBLH96urEzXL2vQ5WESyXkerBOBnQvA5BfNk0CcNkAdK4tN0bpOvysh2Xn0Q90FZArtIX6js4ArC6tFiLlIPOJFWS1AWjlmMoyHdFoUnupk49u3NMPjnG1u5q3jP2jT2TNSADjk40zpvXy2jkhyLwH+5NjwOc9iRQvUHHilaZpVtur0iM2hbnOetYlNzVNlnOJq5r3lg1pNriBfvvzI++JzXCmvGwIEkABkN9Nj9+T8F+3iLCy40k8ulHsNlXPB5blZNYZNikBmO7JFXSPx6dWReT65Kzk52MDcKlm1HawrRyZtcwtEyFr9OnXkn7V0JjIR/suumeMCpUhSoZhaqO8oLdDuyQAPZ5PGtTx4CsBuGJAyT5RlwBM6j7dnqSUUyr5jy8Z5TNNAlC1Aya44hv5HIWq36cgAcw+6h9ZW3lkkQBUSCNWiewSQFbagOalAbU+vhKA2rF1H/3EitRC2IxBcjChZJk4EhJASl4qXSZa+3rddPii3p5VRQKQ39nWRvAb1Kb+keCDeTMDbpStL1pc0NOlMYBqLTmRqn0zwQBMeThUQKUmJABfrxtTukqPhQFEf3V1l3XvgVZOkABmAbUa8N45MYB1Lm+TAGxeDLoEIK9NxlKXeGuCj6olDap6pRUGYJvcXBKAKZQuoLVXKclkJN16+6W1gY2RAOK8ZD2dDa6JxqQSa2UTkAmueqtF5HHAja5G0etQqzVCXej31fdiDMBQTs3BAHQJwDaRu1RAWSLwjk/E30mDKV1fr3lvjPyrjzO1v5w8B5y9FE+vlqPeO3vZbGvIGd3FAG7dFbsP1TCvCQagDwSL+kG3Ach8StpqqmUJwKNj+6iAfHZLVhUR2WTgjNWrFjd2qXmoOlAVsfZySAC66iNtdaqrYNR35HnJejmm366J1fc0qEQZ9iwTqGqSY+xd5Ydtp2oWyOxsE++pC8LtVD8bO8Y4YPYCUiFVQGr/U1Vn+hhyuY2aftclgORrsfQ37wC37zsSGWCSAHrKloUCN56rUBnmtZsNN2BTHVRcuCoC7rUZ3cUA6lv4y40Ok3Dl87ABMEcd1yABqCtb/VmtZj88RHXfzFuXbPLEMEG2T4/ix69OrmrbnL0iJvoH2uYqk863Dq29dCYjmYIuAWWVABgKA3CogBIMwCEBqGm3rQcWD1ncWHVaPb5lPbyFbny0SAC2DU1ZoK++9baQq3/dDTrWtz1sAFIF1GtgxPriqEQwHrHqYqo+RuDzV4BjZ5LGexvN9bwN/bhHC/+i8z6975sWDpevJyUwNY9ZRHcygJ4yGjpQJQiVabXp6wUkGYlJApDPzl81HzYCCOPRzbvmstLAsE90dQnAwQAei0ISywmw0mNWz8SMxNG1fnZyrdbYpKWHzk2TAGRsJV31kbZLNaGCUJiVi/FlkQAAYMUSEW571cq4qq9VCcCkfkyogFQJIC8GEF2bvICkQTzxfXUJAMlJMJY+WhBUFAagHsWqq1HTJIDpmfgiyocBjGpeVt4MwHCvXNZo1PqAjw3g9IUkAyC1IWcPXcYAlBgu6qoTgNV33MgA5HsWG0CaEdh3RetKe+uecmoXpzAAgx5WxbLFIvZ6VZk0bef1JvI3eGgwAwsXNAzjJqgSgFrW7Xtxt8hWJQBbvW2rVRcqFRHPX6ff+b18GLjWDvKerd5pfvHeUKRWGwNwSgDwlwC2PCL6GCDO0ZDpdQnA2MeU65ffEvsn1PJlFrb20u0WMZWj5R2bd1GaBFDyNQIbyg0SQJuhSgD1D6YGoTIxAH1Fx2YJoM4AtA7GSGcArlWjrVMfPSVCIsj05ZJ5tSsnQ5fng76KqvQ06qkWbzz/WJeY1PbRyowN9ohRqhOdMbKq5buoSExA3Bh4WeKspE2sPjYM07dMswFZJQBDGsDfYyetTFUFpOcp219XWZpWri4GICWAhQvEwT2D/Q2mkpAASulHSOogbSFmgtquqvpXf5bI14ByZANI2D0sbaC2a91OqDBcGaBQlQDyVAGnoDsZgOpzq9oAfAJiyff0jic/rm4DMEoA0d/pGXGikO47bNP9psEkBUg3UJcEoLuhSXWRHpbBJp7HJqdaQ73gmvj6ekW6qWlzh1ejQ9a/lVaHkqK+k2mBuDRHpUboAJ1uHaneNTYvppR8fRlAQvVlYSzvnhFGzVbAaFSHtPwvXRMGS8CtAgLc6hfmpBtof5+dAVglgBQGoG+0HJ8QDh8Sap76rmEbA9AXDju3iNPe9LPA6/O/ZfJW5xRVrSrTqScE6nnNArqUARgmFZ+VZvQqALcEkOYFJMu+eVd0VPXQCfV5rMAUoghmBiA7W9pKWF+dA4gdzgLYJQB9dSpXvS4JoD/aoDUxae7wJQMD0A1stlg86rcskTjuU6puJJphAF5eTAbpLo0BGHdEp0iJx86480yFJgGo+Z+5ZC/X5AYq0+kMy8S4+3uVndc6A7BIAC6o6zBZ3uvH4sdQqjSr6l/APub1/rt4SGwqrPc5XfWp0aCXrbeNLLf+DZQ0eUh4nugyBiCNT4BTBaRPNKPjjVW6KgHExFclv4QEoNEh85C7c3VPGtP8/+4ZcY6sCRwRYNK5+8Q/sUkz+qC22QBY/81mBqBC7tCdmDKvwuoSAOz6fN2FNzbINBWQTotR3Zdh8rGFsjAyszQ1lCWftInKyJBrwFsnGzGMrEVyfNHiWsXH8tclAAu9khYgPp5iHmbawqREZgO3twRgo1npX/o+HV8JQC0PMHx7y+Ste3jVTz6LftelMKU839DcOaBrGAABjVOPWOEA6iqy/pE0XfqhY+Jf/V3YV7dZbAAzlg9tmlBu3BEHj7vQ35e8V9UmQhtiKiClk6r92VcCkKvLitaOMQkgYlaj41G8mLIIpyHhJQHoDACN3/UVVimetk6noe1t38NEv68ROIsEEJs8DCvqBL2G7yHDJNePbbRAzc65E1ivj8XFWbdZnL0c31fSeCHOsGNSlec+AL18XQWkv5eQAJQ0LhtATMJX7hvLstCqR9dV1atqfmoeQQLIH0uhxah3qYBsfuzTSlx4105gXy+gqrahx7Qj0EsfGJUhD9VQ4RsB0aYCSlstmdLIOq9aKVwnTSiVRPC8Sx8Ir59yGRhe3vA+MX2XhARgUQFJutQ0OgM0qoAySABUgvlcYkO+PjaA8cn4xh99QjXl67rnU6ZrH0A9nfbbZAOQ6dQ8Ll4zSwC6ysjkGGCi1YbYRG1Z+ZtsADfviMidLhWQiQPoDMD2V2JaC1oo26KqMQDVCBwYQP7oUz+mOrhiHdLQYVWoIQ5s8TsSqykHA9BXnDYvnjTINMsWASuXxZ+pq7Dh5cIbwwSVRpMBVs1LxbnLwjVPom4DiMpct8pcBgCsGY4mv4lGmTJNFiOw0QagrT4TEoCJAWSQAGIqIEu+WSbjU+fjh5GrUipgd0fW4Svxqatv105atU0vXI3TKN+Vz9W+snSRWQKIqYxgXngkaHDUw6QCkjCqFqOF4LEzwk5gVQFptKRJAHVaWbhVP7NTLGam5LnjmnbBKQFoNI2NA2+ewKI2TNfOE8GIqB/AdwP4GIDVAMYBHAXwl8x8LHdq2oiyOltXa8AHkZeDeh5nfaLRJmLp+zszoxlvTBIAmSUNFfKWLsIbYwYZ3jflTxENDy8HbtxuPFcDYD26URie5Y7jvTtF55LvS9hUQD4hCFQJADDbSeq/oxvTM8ACTXqJfRfLpObjBWSTAIzSTAbda33XskFVI/dnrB02l63DpD/XV9S+Zw3Ib+QT70aXAK7cAFYuSdIGiENQzl6256Eyu4UL4kzYJgGoweAAezulSQCwTMomBq3SYrM/yXxN5Ojnfsu8VUZZiqTx3kry7Iiy5kVkkgD0ekxOA/dHUGpDxDgrAyCinwPwPQAOAvgWgOsA+gFsA/ALEXP4p8z8du5UtQGxKf3C1ca1adJNSAAGMdfaQUraKhAOFZAlyJXLGAgYYsbIJbehLH0Vpj5eMKBMvB4qIHlweJrrn1XvrU/g1KBRt7vUv0E0UZRKBgZgW41xcoWVthfDds9Gfmzi4/i+Brk/QzIAXzdQFfrk6FpISNwfBY6fzVCm0ja1mpBCbt6Jf1/513ZovEmlI6UjowSgjSWT5Gmk1QK58DGlMx1O5ArWFss3sVqJ36/nqbWT+t1UBiCfS8ePas3QJyVNGlOKForTbdgp5pIAXmfmn7M8+2UiegjAutwpahNKetyS+gO1A8t7FvEvpo7QdYRKfr42gESUQ+2vvNZpNkUj1EVUCclk0iYEkyguPXrKkXFubEK4mqq2EB2ZJADlWh+YuhGYKIMKCEk3Ox8VUCpMbcSNNkocZ6nRYIPOaNV862k8dOOq5JcqdaBRHTXt9IyQeKcVj7npGWGrMcHGAKrV7DYAU+wdmRZIBqaT+dXHp1Ifuf8l9gBJdZTLC0htX5sKSB+zap16K40IpLoKqFo12BAVJqIiGu8zbWAAVjmRmf9S0EWf1p8R0aeZ+TozH2qlcCL6BBGdJKLTRPT5VvJKQxkwrzDIpAJydER1ZWlc5BomJZcEoB5CYvMqSaxsNAagPk6VACwTg1EFJCc3ZXLu6zXXW62DjQHYjtAEkgfWJBgAkhNEWW9raO9QowzfOO0umCQAafNwhSU2OQuoSJNGXBJXrYYVUr5VvcBkX7JttLN+I8T7v9T9W1UlMh0aTIVK8QnYKAEYGIBVBRT9feWIoXxCYlKux/cybNQraf0x605gmwSgSgIyTaWn0f4mL6CEYwKE0VjffR2p9WaVASj4gue9TCCiMoDfAPBJADsA/AAR7Wg1XxvKiFaQic6udUgAKDt0kWonM3US/WB4daWl5nPrrohpYhTzNRFAXxHEGIBWiF5Wq15AQHzy7O91r2il3cBDAxQX/y0qoPrEQnYbgKnd9N3PtnNp9bycMKgr5KrfpI4ySQCLDEZ4ow2A4+/bwlGfOItdNCCkM7V9qlWxcnzliAg+lnzZLqWpbWVV58n0WVVAGv0tq4AMDCChp9eYqQrnPgAD4/aRAGT5fRVBw4xBGtJVQBRl8vIR4MTZOC2RBJBXBCgVLhvAJwF8CsAaIvo15dEiADkEJMezAE4z8/tReQcAfB+Ad3PIO4G6BKBLkfWPoAw41wEWunuhDnXyLEf5miSAuw/E9UAf8MjDwN37wJ0HdVIaaZHspLHncpLQ6xNB9wpJW9kAyuQrVUCaBGCD3GTjWl1ay2xGBWQZjGPjgsGqE1maBCBVOC6oVSnpDCCjBKC6POpSXP2e0tdsNoAbd5LpAfHdZf7XbwNb1yfzN/YZiuJK9QCLBkUM/USoEkPdpC1EfieuJSc9PX1CAmiWAVho0lVyJlhtACWADP1BZy46jaoNoC+SyNQDqFQJQG93G6kzUi3nqkhzcNkArgA4DOB7o78SDwD8tzmUvQaAurPpEoAP5ZCvESXZsXVx2rThyNUR1VWZaZJLSAAWBiDL2LG5YSySB1akqYASz5VnNhuAyQiswji5cZxWQIi1tkld1YPX28ZBW0wFpDMAy8oylsYy6UqDpWorKVsOJq8/LyddHF0oKSu5mASgliHTGhhATxmYMhgpVfpYecfInFSJR1mc9Fbik59RegDqHyexmGFgaCDJ+E1nWcQmdJVehR4TA1RVexJWuwW7VVBWCcDwjr7qbnYncMLupPytM4BosTShtJvNCGyteiRB9PS0hQEQp3BIIqowc+5FR7aF72LmH49+fxbAs8z8j7R0LwB4AQCGh4f3HDhwoKnydlUrqJR7sAAl9CgNf5jHsIcGcYwnUAbwKPXjHE9hAzVWujPM6CHCWZ7EA9TwBA3gDR7DQpSxlfpQY8bXIcI5PIwePEr9eJVHMQHGhzGIO6hiFTXioR/hcSxFGWtQwTei9zagF+tRwYsYxTpUsInE6uEET+AeqvgQCdXBQR7BUpSxm4T3zqs8it0YwD1UcQKTGAThWWqoGSa4hn4q1elZjBKeosF6XhLb0IfVEY2v8ig+TAtwgiewChVUwVhGPXV6NqEXvQaD3TjXUIZgtlcxjTOYQh8Iz0X0nOZJXFJ6sUrrezyBK5jBcxhEH5VwiiexlfpwgiewBGUsRhk3MIN1yne5wFNYR704zhP4QHlXhazjIpTwdFRvADjOE3iMGkHiRriKIXIfmvM+T+JCRP8ylPEEDeAwj2En+jEBxhIqx/J9iUfwbTSEWzyD5VH7yetRrmFBROt5nsIylLFQKf99nsQMGNuoHxNcAwMY0Oom+668XoIyNlMfrvM0FqGMNzGO52gBmBkvIh5uZDf6UQLhTYxjNXqwLaL5HldBEPrmaTCGoz4xxjUMGtp2Ocp4nAZwiMewFhUsQRm3MIOHUMEFTGEz9eHrPAI5zQ6jB49F4+MZDOIypvE+xAS5Cj3YTsnAfQ+4ircwjo/RUOLZJZ7CHVTrNIyghmcwiAVUwrs8gZuYwceV997jiXpdx7mGG5jBGlRQ1hYn13gavaB6v5f9aAgl7KVBvMPjuIUq9kV5TzHjZYziY1hQr1MFhI/SArzHk5hGDTtpACd5AtupH1PMqILxLYiDd57CAGpgLKX4QuUVHsVztAAPuIoXR25gaCjZBj7Yv3//YWbeq9+3MgAi+gsAvwng/9UZABFtAvCjAM4x8283QxARPQfg55j5u6LfXwAAZv6Xtnf27t3Lhw41Z3e+f/BbWLRsqYi7o64Mn94BvPGu8JGvRq5wG9fEfZ6lF8y6h8Xfy9eBpx8TbnenLwhO/vE9Iu31W8IV75ldIgrlK0fExhAZXREAdm0Fbt8VovlHnxL3zl0RJxd9fI84OOb8FXF/63oRiEqGonh+r1BvyGBXz+wC3n5P7Kp9dKPQBb9+tFGW3MPw4SfEiuTeAxErRuYl8d554OoNcf3cbkH31nXAtVsiDxmDfcdmUWeTR8aCAbHa4ZrY5LVprVg1vhp5Cm9+pOEaCcRp3b4BeHiFKHdqGti2QZzfvHW9UJeNjIpNbqoL7/pVoq22bQBWKe+qkHUcHW+0ISC+jXruwKIF4nu6sHFNY2Pbnfui3XdvFzt4B/sFnds3iLNfAeAjT4pNcsuXNKJTymu1vLXDUR2VM3jXrxahNE5fFMbdWi1Ztye3N77l7u3i2567AqxeKfrW048Brx2Nt4PEWycBMPDko+K7y4CEiyIf/kqPOP/gg1vivkkCUPviU4+KcXF/VNTx2g2h2jwX9Wk5wX5wS+i4n9klvsfaqJ+oz3QsGBAH8ZiMwGuHRd+XNCyKxsrouOgXDy0FXnpTpO0pAxvXijEOiHZdvliUq3vkPbxC1Ff2e9l+I2PiqMwdm4AVS4GvH27k/dGnxG9ZJ2bgG2+I3wsGRN0e2yT6S6kkbATPPh59jxMASHxDnY5o7jjII9i3b1+yDTxAREYG4FIB/QMA/wTArxDRHQA3AAwA2ADgNIBfZ+Y/b4oagdcBbCWijQAuA/gMgB9sIT8nSkCki9TVCJpICthtAPI8T8CsewQMHggWFZD0bVfzq5el6RFysQHIsmyirfJ+zMNFo7+nnBTb1RAariBXetEm1YiEvhPYRwXkkmZ1FZM6+QPuE9NMNHrZAEwqkOivekKW1QgsyyJgxlA3fdex6mlSraW4utrsNFJFqvXvNG8ZSS9BccN02ENknfVgcEZSLXnJ/Op5avmoGxk3rRUM6aqyEJMqoFIJCROrbae/be+JrI9aJyLhNDE+2Qh+6LIBmIwAchwsXwzcTAnw1wSsDICZrwH4GSK6COAliE1g4wDeY+Yx23u+YOYZIvqvAfw1hLn0t9u5u7jhBaQ9UDsd65NlBFPnc+0DADQGoCfipJeKTKPr9Ks14J1T2usmG4FB566mLVme18tXOm1sABkYgLopZu8u4LV3xO+yEmrXaCdw2ABcm7ysRmCH4VVH2pnI1nOMU9K7GMD1O+KvSQeuuv/q9BOJ737hYuO3KUKkqR+UqBHvxmXUthnqCWgwB+W+LUxGbPJVGLUsXz/wXF5Le4uvEdi5Y1eblEn7Nmo5erecqUb2GE26Uutvol/fu1GtNaQBNf3ggJBGlkTHpPYYFn2SLlMVZb13bAa+btiJ3SJ8ev0wgD+GMPw+DMEEcgEz/xUzb2Pmzcz8pbzyNaHuBZQwRCoDNy0YXOw92wpBc6FkWCQAzQ9YXxlJ3LxjME5qAx+wSwA6XVavHGWA6JOZ+oouAahtZZJoXBJA7JlrcrcxAG3guxhBmpunlwRgKFuuMk1GYOl+qfueA6LdntvdiE+v0k4UDxGuSlnq4erqpCj97qnUqIvLqK32S5M/OhD/PssXm/MxuoGqhk6t3WWWR04myzB9I3mCl9MIrBEeW4TpDED7FnLjm16fNG85kyOJngYQqp/xiYYzhtrPTMZ3HdVonvByU86O1FyZ+b8HsBXAb0Ho/U8R0b8gos1toahNEBKAYZD7qIBMUPcBmCYGk1eAxPGzouPFJk9lAlE714jGb/XJwjR5GOm1TfzyuXLhUgGVNQmgZGEARokkgwSgto2UovQ6ZJEA0urvJQEYJquqXHlrqj9b2fKyVBKTuVyts5Z+bDz+W9bx0Y1C9w9oKiDFG0nWxXl4vCoBlGK369+87s1SiYfqNtVN9wICxKSnf1fXd1D7z9phYddZvjhdmlFDtahQVUCmBRKh4WFTp0EyRcv4NtXXxQCGolArkqHHJFFtAWjqxjPVJBPNEV45s7AUX4v+zQBYCuBPiOgX20ZZnqjVhJW/p2zvgOrE63OOrGlVB8DbBnBvJP5hTR1Lzaf+W3vOWg831c+0Mk+k0d5XY9u4bADqxKkeSGMbcCpMonWdnmig1NvDIHG5NvwAyfMRPvKkONpP4uHlDVe9zBKAZSOYSVViqqcqkdU4ruKRE5Nelk6IWl+pcydq1MXFAFTVZJ9qj1BsOPL5QL99UZQmAaQxANWwrJ4fsWmtMOLKvSVWewbFaQAa4yOmprGMj+mZuDrOJilIxBZ4Fo2BWmcZfVdu+swsARiYaI5IZQBE9N8Q0WEAvwjgmwAeZ+b/EsAeAH+/bZTlCSkK91bsk5C6kk5bLco0pnRyMKlGRlt2tg0yYPtH51p84CdUQIZ3MkWGlO9QfFCrealp1WeqesLUNgn1m4cEoDIh2/v6wJd4Zmf8d6Un3ua9vbAOYiMMEoBuA0iTAHQ1o1z5qYxDpu+tCM8WnYHIn3rMKWlXkrSYorfWasLjZ3K6UR91c1/NMA5c48HIAKJ7ptWrnpc6TlTDeD29QrcPDeKi8SxtfMzMaBJARK9qBDZJAOpCTO87ah37+0T7yrORbYsemwSgn6mcM5zhoCOsAPD9zHxevcnMNSL67vaQlTOkgafXsIlJ7Ty1aOL14bg2G0Bfr7D4330gvA5UA60Okw2grnYxeCaozyV0lZFRAnCstOv3pTpFSafG9je/lF6Oc/IwlK/m1dPTCDxnY7iAXQVkGji2QefFJDX6ACXejfbbVmbCKE/JFW5d9dIr3Br1CUjtK2q+LhWQZA437jTcfWU+sXhUaqyrDN+wrjpS0vusXjeuaVzbdPFpKiDbjnBlkW6sC7OYYCs2G4BjEaNKAC4VECCkgMkp8V1sY9HWTG2WAFIZADP/rOPZ8XzJaRPqDKCCREvLn2cvi0MsXBONCpsXEAAsXij8o9MkCmuQrCh/kwSvq4h8Vms+k1tJHcmIq4BA4uwAuaNRlxae3C5WOrfvJevjUvO4JAAiwbBlMC2TWx6jMUEAnrYQW/nN2gDkhKn9VhHzNNMmDdMEl2g7faIwMJuYCijKWzUCm1aS9ew1CUUv1jUBJVbfpLRN1T05Di8XDM70TL3nVAGZaFD+usaHZJBGG0Cso5jLsi0e9N+SwepOKLqaybSQmanGJeuc0T7ZopMgT+WppKiAbt/zZwA2CQAQH3ymmhQ/dbj2Adho0OOb6DaBpiUAWb78jQY9RMKbQfcEke8sXhhFCfUox/S+Lb0MkSEnetuKzDRwfKSWEin+2RklAPmOeuAO4KEC0lyNS5RU6+kMVn9mui9j75TIbAN4+a24Z5FOl0qfbO9WVUBGpqPk5at2c7qBKnmaDoCBNgZNDKBiUAGR4jJuWjSoba/XQ5+wpWTjVAHBDJMdJUd0CQPwUAGpv70nL22gSuh+2M1IALb3ErHjpSjqoNkWjTGWRpMA5MAzZZ3GRNRrX6Ygy9+2XuyqrfREDCBFBUQk2vnoaa1dfCQACKPwzs1xPfjTO8QuzzSUSvGTnYjSjcAJTzNK6n71tpO7UdVygKQEIFWHdRWQZgO4fttO154dYhdwbCOfaQa00FpnAMq9mWrym6k/fdVujOZsAKoKyGQElulNwQ5dC466etRiPxrQnA9k/vp39ZEA9P1COaM7GMD0NGaY/Tw99I0rLtiS6SswKwOwiZYOCUB3+9T19GkSgHVi1CUAstNiK8tnRWODVKEsXyLCBEg3SakCMkpckWpobKIRasFEi5XGkmA0K5bG7y8cFB5CrneBiAHIb4wGM3K9Z7IBJA5a196LeVep0oRmA5A2LNn/ZMRZ9V2bvWRoUEhy9YlNlQDMr8ToqS8WKG4Qd3kB+Ton1GqNQIlpNADKpG9QAZlW0+ot2dYxI7ChDmo76vXQI+bWbRuGb6/SYPs2vvNRE+gOBrB0Ec5D019LNCsBiMTa3wjyg0+nMQDDylyddK0iujbw0+AjAeiDSK68jOoon9W1ZfVkpdHwTEa1lCtJ/X054Zg2PKXVU7/O4q+uvlNf8UcTRlM2gJo5vaThyUfNtKkMQLqSqkZgE0zeRvVyS4iv5O3ZJGitRpuu9O/kUgGZGMCjG0UcKT29zuBjeUZ/TbYgXQ2b5hgQkwBs5SmLI8DtBQQ05gNdsteLsNk5ggTQIpYvwUUZhTKtU2dhAFYJQBPBbelcKiCCmQ7jPgCzxFAz7mvwYEaSnlQJAMl3TNcpRSdpjFCXpGbEu4sXipWqeoC8lQH4SABNrEZjNBtUQL4SQN0NFAYJQFut9mkSQH3C091AIxWQq+7qYUJ6sthk7hgHfZWGnrvSI77T+ESDcbhsTjEbgCH/4eXAlkfs75tgjc8Dw6Sbkp/a9q52tEkAg8lopvV+7JJGXNJZwW6g8wweHcCbAVjStaQCiv5TB/qiIdGxrt2MBrC2wrH0mxoiDt+UBECOvC0cwFff7yrfdE96t/SUha76/UsivorclDbt2PDkgu9EZaUZjUnYlwHUY+RrXkCJjC002FRA9bMbXBMXzN5GEqrqRl2AqBNWjYEPPRHPQ0ZW5aiQ2O5wR7tmdb21ptEkopgtQLFpmejRaTIxikQ7aWNDlQCe2ZXMv8fCAPR8s55NkAO6jwGkDWxbFMAsSHhhpDAKlS7ThrTBfrE1/tpNgwrITkb9kc9OYOMqP28JwNGwRtE8+qvTsGG1kASWLfabdH1oTExU2jtydavnpR56QpTBCKxMApL+Rx4GHhkGjp6x10G9ldgI5rAdyZdjEoCFAaj1UfHMLhHZUr8/2C8M1XKnvVMFZCnPSnLaYFTKM0oAPiogiNDZRI1wzGkqoJrCXEwhZlT4MAC1LwENZmujOSd0hwpIRWp/akIC0JMnVEA2BmBYKZmMaUTxs0R1HafFaFwzhSO2IbHK0CY3/VnsL5Lp8li0uNQ1Dy2L7nkytLQ8E/FWTJOYob6q14y+wq6/b2AA6iSgBnqrVOwrRHnPNOHV3UAjWndsMrwLe1RPnU6ZXkV/n9gro6O/Txjr5R4C14KjVbWbK43RBqClMeZHYrPW0KAi7Wg73vXybBKACTYVkEsCIIcUlSO6kAGkNGYmBmC536OrgFLSqXSZ/KkTDEDJw6Q3jDwZ6t0pVo5Wnl6++tu2j6EdEkBaeturrdx3SgCOZ2oafeesrwQQYwCau7DtG+n3bCogwO7G6qMCks+ck6b6HjXyVvut+qyRcePSZx9AmpMDKfQldgKbvIAckqb6I1Zn29jwXKHLeuoRedPUTPXrIAHMHrIwgDTVTqoE4GIAiHcUKWZWq0kJQCclCoJWkTdj7q8WWkyrP5sEkHV13Sx88rOWk5FG1+rXdp4ykYgGKq991FHyRLHenkaeCTWFYeWp3pP52WIB6WWqaVQVUNoOVl/U6dEWLkBSsjK1qws6Q+3vBTasxlX1kML62JE3XF5AFglAv7TFApLPXLGAdFR6hNpy19Y4Db4q0TzGkq2YtuXcqfCRAHxFLlsy2SHkDmRbR7cxAJm5OuhVDw1V5SOP7FOJ2bQW6OtF/SRWkwSQqItplZOykc3FGLJKAMb8Pd613rflaZn0rWouNZ2hvqbYOa4yV60Uxwuqm4NqyjdX06vvlRVXQhMDqHF64DCZRkLf2Z3wSomTZYVKDwFO9UVWFZBax75eYYBevxrTuvFLlVhdjgvGfmmgz6VelLu39fdthw4RiSM+pYcQKff1cvX3gCAB5Aq1nXcajjTQt2vr2LlFsfRbJgaZx3S0SrGtEEynA6kx+OVAkp4d5XIkAaARV2R0PEnCwgXAh59o2AC8JACDG2hLEoA5SSZ46X/jP2u6yK/DFIEVaNRfToq+EkDMBpBVSjHkoaZX39uxWRi9DSeJ1ZiBmgxJ4ckAVixphCqWSLybon5R61CnR1tAuRiAjwrIsm9hVC5uBvob5dQZqWILSNO7i5vJMkiRAEzjWw81vXOL8FDzgoEWx/oj2AByRdSYSxaa9aRpKqDB/iQnN6FcNksA6sEaWVRAQONweskgtq13EACUJIHNSACAwgA830nTW2aWAFJWSOJB7Fd9urClt7nEEgHP7gIe25x83yUBqNfq4/WrDO8bYKqjabJashB4fGu8f0bfpwY07E2uSXVmRhw089AyMWG5vp9LB26rg2kDozMWkMfOfFUCUOj7ADPCc2flUiVfgwRgs2PFaFKulywE1jwU9+d3MX75fMWS5PkTaUgbD6ZQFTmj+xhAmmEr7aMYjz00pOspmSWAYSXEQNkwMTNQPzBeXxHKQ9eNO4WTRNRzNzEaHaZVvi2WkY/aJdXY5oEmbACpa1ZXngP95gnUJQHErtUJ2+AtY6THkF9qH43+Rt+nCm4wAJcEcOO2SLd6pfm5zhyzqoBkWtcqX80r7ZxmwL1zWZVgiICRMeDt9xqxv9Liapny7esFtqxLfk8VJgmgGZi+vQk+7dQkupAByL+2j0uNydWUxmdXLSAm3bRBaTsQpm7MM0kA0UYw3VZhItUkAdhgEtVt6hTLgti68iPbCynwYgDxnzVftQUA51F7WctOrHx9pBfLM99FSlYJQEp0QwvMz01HevrANZG1Gg1U3/VtpYHEKXtq4DwAyOIKncjTUq4Mla6nywpf1U5gADkibXCpW/RNacoWFYIrna2jmyYZjvS0ZYMEUCo33EAJ7gkMigRgcjdVD+LQaUGUf72TJ2Z6833bBKi/5gsfXqvR7fByT8J3ANo8a1y/fXZf6+/4SloSbJIAPCRYW39MLCgy2gAaLzbgipPjY9xcv6qx9yArI1U8Nb1VQD7QvYCahVOCV373tG+/bvftBJawfXR1sBMlZxRf96zY2Z8ZNrycviD+Ll2YfEYQER7lwTUpq8yyyQ2USHih6DBJADYbgI8EoEs3Nn/uPTscsd59VuEtMADfCSXtPFx5rWbnvZp2MYAU2lQJQD9nABBB5KangWNnGvd6HUNep9lHfQK4JdFWPViIhKpHnqnrSpeAr5rG2tDm23UJoFUVkFuCr6ONEkD3MYBUCUBxz3OJ52lolgGo7+i09kZBwe7cF0aqFFrqpfb4lG9YqVk9anwkAMMK2ETv0KCDJk81igJWPUDS4JxQlGub33aCAVgkIN+5x8VQTYgYQFVdiqrtvngICVQ8GUCWOc31ndKOS8ySv+tVk+TDcPRhNf+U+zYVUIvzv3f/nm8qICL6NBEdI6IaERmWo20tPP5XR1mTACQWDyUPenB+NIcK6LndwLOPx+/pHTjGQKJnapRE/R0DKZdkBFTfcxBsaMYGYLzOOFKKlABi+wB8VCY6A/CUAFxqgFQbgKhtrM4m9c5zuxteLU4GoLe3r47D8Z3y8GDxYRq2NF6TdMZ+WXc5zVECcJE03xgAgKMAvh/A1wsq3974qgpIHRBrh5OTtisvlyTRW0lnJiYDcW9Fo889yZzFFPDxPc0NIJd4apvQrRO2xwoujaY0dUiE+mToM3c5mZ4hnZPh6b99JQCfdra8xykSgESvGr7Zcb6sXl4925QP56qD17GPKfDpNzYVkI8ROK1v6c/bIQG4Jvn5Fg5aHiZPzYiDrSKTBJDS8D42gGY/nkkFJOmTMVdaWRmlpfNafaf81u9n/d4xHuJHQ10F5MMBMtsAHBXWv0cz7e76Bg6kSgBAg4m5JAC5qJA7eodXCHXjI8P2d9S8ZR6xZzlMXj7GelNbeauA0vLX6xT99lEzOrNV8k37Lm0CcauVaKVwooMAfoqZDznSvADgBQAYHh7ec+DAgabKGhkZwdDQELahD6upgis8jfcwCQDYRw1d6bs8geuYwYcwiCoYQyQm8nd4HLcQjzvfD8KHaQEmuYZXMBZ79ggq2Ex9mGHGSxjFIEooAY3wDAaodJzgCaxAD1ZQD47xBG5AbCr7EAYxQCXc5yqOYwIfIuHSd4oncRnTsfxknX0hyz/II/V2MtV9O/qwiiq4y1W8hfH6fQLwvJKHxHMYRB+V8BqPYiyD68QAqF6/93gCV5A8+GUH+vAQNVa1H8xMYLinH2Ncw2vaN9Hr+Q0ege0kgV4QPhKVfZ6nsJ568SaP4Z7y/TahF+tI2GVe41FsRB9WUk89749F5RzmMewhYetQ2wUA1qGCTSSkwSM8jjuo1tv3JE/gqqHOAPAxLEA5mhjOTY1hQ6/I/2UexZShjZ/CABZTGad5sqEaNOCjWIAKkbE/2bAEZTxJwl3zfZ7EBUzX2/gVHsWkRs8+Qx9xYQ0q2Ep9sf6m921ZPxW3eAa3UcVW6sM3eaReG3WcAfY224herKde3Ocq3lD6uRzbb/M4nqABvMXjuGvtSUnswQAWUhlHeRw3o/dWoIxd1HB5HeMaBqMFqGynrONZxf79+w8zc0Ld3jYGQER/C+Bhw6MvMvOfR2kOIoUBqNi7dy8fOuSVNIGDBw9i3759wKnzwJUbYjPM1mgn7YtKnju3iF19rx0Vq6mRaBLZtTUZO2ViCvjW20K8fm53/Nnl68Kjp1QCPva0H5EqHY9tEod437orwgDIHY+HjonwD4sWAI9uAl57R9zfsk7sYDTV2Rey/Of3NtoJSNb95DlxNsGShcDu7Y37zMDXDzfykHj1CDA5DTyzExhU/LrTMDEJfCuq39b15g1Mx9+PHXZ+g2fEJNzfB3zIorKT9fy2p+w66qlp4JUj4nr9auD8FeFVoxpWz14CLlwT18/sAs5dBm7caeT90pvi+unHgDeOi2vdA+vCVeDsZXH9xDbh4XXirIjxtG0DsGqFmb6X3hAuwUS4VJvE2ogR4SNPmleT33hDrOyf3C5OVrPhlSOi7ob+ZMW9B8BbJ8X1xjUi4J1sYxM9566I/rtMG082XLkOnLog2j46HjPRt9862YjlL7F0kSjjzMU4HS9qc8hzuxsqMhVnL4vvs2gIeEo5llOO7R2bgXfPNL6bLw6/K+YVOdcA8TYEhIr4iW3CvTdylMg8nhUQkZEBtE0FxMzf0a68c0Ha5qyETteQ1iWZyQ5lc3FMg80jw2ajyENK7O8VYr/M35a3p0E2mUFmI4BH3nF4qYDkYfOZvYD0NL42AE/1g83Y7iKQPFVAsh+6vK4Ae12dpDjqYKJnw+oMmat5NqHHN4WCqPSYjxH1zVsNfx2jLyNiKiCdAVH20BJNoPvcQMeF2ie2w1BF2TbBWnSMNixxrLJ8oA4cNtzX6cuDA6hH/amwegFl1HO3MP/nuhHsqUfFfgqnftpkA3CUbbLJPLcbuHlX9LWnHzOvzJuxG6jkEcW7oS2P7RuAW/fSvXKs9g4XLa3bMbzzz5LGFAwOEM4cx98Hbt9z52/r57KNXIfr+EAdvy4bQBtRlBvo3yOiSwCeA/CXRPTXs1a4VOnokRAl5MddssgtKgNoeBgYHsnDsm2MJg1ESr7KEI95FzXpt+1bvuna9Ds1r+hvVm1jK26grrL6+4CHLaqVer7Ktc9OYJMRuLci1FZEor+ZVnS+3kK294ga4S9ckWwfXmGOfqvDFvfIhxbTe7PFAGTcLZ1Z3zUc8dhTNqt8kgWby09IAB5ZObKv01QAivIC+jKALxdRNpYuEjpjNdqfCrnC3vKI+MAXrorfzk5oefaR3eb7PpChdW30EdoaJtZrUPsWL9NntTc14YmUyQvIt2zXiWDqdTOfo2kJoDE51Wuah8ulLfKpDy36dV7wyXMyYgADfY0Q6eMTjfuJPC3XpjQJxq9LADmogOS16oU1C+i+WEDbNwjRXG38HcrKyKrHNeQln9u4d6nUvBvcQJ955VxWRPR2DrzYALGIwFl7aVsYQBMSQFZI8VxXn/jaAFwg6w/P9xUGkIvLZYsSgHzx0Y32qKNZ4bPgkDp9VcpS+0Die2RQn+qPc5MAtBc/8iTwxNYmM2sO3WcDKJWAXm2grFwKrFwmwuW6TvXR0dcLbH7Efv5qs9ix2W4ASgtVkRdck1lWySMPI1mqgVmgRa2subyli4TdQJcaE/Q1N4E3rpt4j6hR51wZQLMSQPR3eHk89HkryEJLdB42gPiCw2Vgt2ZvUwHlJAHo71V6UD/6dZbQfQzAhu0bgHUPxyPv+XS8tSmbZJpBRZnkgXhHbmaF1hQck2+aSmeR5qu8fYNwqbOp3TxIsEdUjf9si1MzUbJO8r56bVMZpOVtuk59r/FOXe2VqwooA1zSYh7wybOnRxx4o0ppNVdvyKJeNEzUQOPcgTyrPEuqH4nAACTKJf/AZO2GLEvaK1RDspRQBhyr0VxocOQtmZCJAXzb08lOvHCB8GnOTIOqh/fwxIFyHkCeuzR91E/NSmTNSgAmN9BOUAG1hQF4pNm7Q5zAd+tO457sAyYf/Uz1037LMC7jE01k1lkIDMAXs8oAokH48Apg+ZK4i9iqlWIl7buJpmkaHPV1SQB5rEJNNPicqYAcVUA+sBqHM+TRrA0gJgGk0JMFLbuBtk6CM38b+nrFv9t3G/dk/zRuaMvA4E0LoP5eYGwikdVcQ/cZgecCXP7BPWXBFFw6zTzgYwOYzTAinqtbTlw0CS/fcy19/Z3ZUAEZbAB5RN5sdSNY4bOhUr4rDlDT9Ysw0N/8Js8OQmAAvihCBZTtpZxpUK8zqIDahawqoPZYA+xlx2wAmTJJXtrOH7DQULcB5CkBzIYdox3wLb4JF+MYVGNz1jpLla7L93+W2jGogNJQDxswi2U2pUtuIw163s369beCsl8FZ3VNZnUtbHHy3LhWrOZl/CfXezEJoCAjcN4hSVqBcbXfLFEORtzKMY1b1wEPLcvuFNEGBAkgDXLH4Ix/tL+WkWUQtm2l4KECcnpZ5IzZVgH5wOYdlSkPw/uVHuFe7Kqz0QaQoxG4kzaCZYGpeBdTyKrqkzCds+2Lcrn9NjxPBAaQhi2PCHEvSxTLVpGlQ0nPpbwnPNegpg5SAWkoTAUENLf6zWF1WvhGMBXtYACZ+pmnBOBDpsJkE2jXQe19kWrp4Zz2UKQgqIDSsHihPUhau5BFAli5FHgwmi26oQ9cdr26ETjfIp2wTixxItqyE9iGXCSAFo3AJS0WUKvIYn/QaWk3PG0iyXvGhOl51U/8MjEAVQJIz8oblR5xit8sITCATkSWAbV2WAz8vFcMneYF5IlibQASGdqlWRdKxVsn31hALebRDmawdJFwf15nOl7Eo/ymvYAc3zFmwM3bAWP21GiBAXQi0o6ijKUl/4M7MtHgUgF1LgPILRicD/QJNy8bgNd7BiNwUaEgVLRrJ/C29Z5pLe/73NNRhAQwywg2gE5EO6N8+oKsP4pxA7VBI2FWVUD6znHZTJlU1i5dmwPKRF14KAgVeW4EbAZ5egG59hHEbAAdMF6bRJAAOhFFe1IAMPqn1393rgRQA8RW/fUZT51qBkQiSNzElLyRPQ/byW+p7zUkgCmw8CzRw4M0A2pRAugA18YEfKUCHa7+rUoAnbBgaxKBAQSY4bQBRJPEbLqBeoIBceLTbGHRECBDzTQzaTbrTqiogGYAcQZxHpA7z5s9oCSP3citYFF00BORMoE3aQPwVQF5HS7TmQgMIMAMl266g43As+uapKGZhWAlY/RZiVZ19TYsWgDs2ZF+dnCnYnBAeNGcOCsCKQIWtZCPGtNx4l+pBOzaYj9ZcI4gMIAAM5oNBtcOZDBuFsuSWlQBZSqqCXdN33ybmfyf2NbwYS8aPvszWjUCAyIm1xxHYACdhGd3NQ6tLxyOAZKHt4kvPvok5oyRrU7mLLChZvz12wlTyOUikba/wkdv71IhzRMEBtBJGOjPx5CXB1zeHLNp9Mq447JQCaAuGc1iWfN3bsoPTbuBOlRA8wTBDTTAjA6IVDjnMJvt0i4bwHxB2v6Kjoi3VTwCAwgwQz+HQIUcEM16iuQJzQ7RiWbptqBdNoB5g5Qd1kECAFAQAyCiXyKiE0T0NhF9mYiWFEFHgANpk/uOTcDTO2aHlrmCZifjZoyJc9j3fNbR6k7gecwBipIAvgpgFzM/AeA9AF8oiI4AG9IMvSuXNc5G7SAUawNo8r2dm4GPPZ2xrPk7KeWCVBWQz9Tn2Ak8T1AIA2Dmv2FmGb7yVQBri6AjwIG50uk7QQ1VRwvxc7J6VnXwbuzOQMq3CCogAABxwR2IiP4CwL9j5t+3PH8BwAsAMDw8vOfAgQNNlTMyMoKhoaGm6ZyLaLXO+0i8e5BH8iIpd5QArEEF69CLChG+OnIdlQX5bGLKWv/lKONxGgAz40WM5kKDDWtRwRbqwyWewlsjt0Pf1rAVfVhDYoeu6fstQglP06D1OQBsRx9WUQUneQJXkXO49SbQynjev3//YWbeq99vGwMgor8FYIrf+kVm/vMozRcB7AXw/exByN69e/nQoUNN0XPw4EHs27evqXfnKlqu84tRWz+f6Dedh1eOAFPTeJlH8ZF9z+eTZ9b637oLHD2d7Z1mcekD4MxFYPVDOHj5/dC3dZw6D1y5Ia5N3+LBKPDGcftzALhyHTh1Adi9HViysCV680Ar45mIjAygbfsAmPk7Ugj6HIDvBvB3fCb/gIAABXUVRhg6RqR5SfmogFatFBP/bJ4GOMsoZCMYEX0CwD8D8DwzjxVBQ4AHnnwUmJ4umoq5g9m0m4T53w+2T+LjRUU0ryd/oLidwL8OoA/AV0kMmleZ+R8WREuADYvnkF758a3A1RuYupyjvWL1ymzpCzGcBw7gRCsSQBegEAbAzFuKKDdgHmNoENi6Hrh8Nr88t3qeQlUIZjHsxFxEqgoo7IEFwk7ggID8UIQKKCAFloYKG+kABAYQEJAfZnVOCfsAvGD7JkEFBCAwgICAHBEkgI5BmgooSAAAAgMICMgPszmnyHDdRR/C3ukIRmAnQu8JCMgLszmprFgKbFwDbApRVIwg7W/ieWAAQDgQJiAgP8yqEZiAdatmr7w5hxw2gnUBggQQEBAw/1CXAMJE70JgAAEBeSFMNh2E8C18EBhAQEBeCHNO5yEwZScCAwgIyA1hsukYhE/hhcAAAgLyQlhtdhDCmck+CAwgICAvhLmmcxC+hRcCAwgIyA1h1uk4pH2SoXxOj5urCPsAAgLyQpj/OwdS9eM6a/nje2aHlg5GYAABAXkh6Js7D65vEr5XUAEFBOSGMJ90HlwSQEBgAAEB+SFwgI6BjwooIDCAgIDcEFQKnYcQ9tmJwAACAvJCmGs6B/JbBAnAidA6AQG5IXCAjoE8KC1IAE4EBhAQkBfCXNM5kEdlBgnAidA6AQF5IdgAOge1mvgbJAAnCmEARPQ/EdHbRPQWEf0NEa0ugo6AgIB5ilqQAHxQVOv8EjM/wcxPAvgKgJ8tiI6AgPwQJIDOQV0CCAzAhUJah5nvKz8XoGGyCQgICGgdUgIITNmJwkJBENGXAPwIgHsA9hdFR0BA7li8sGgKAoIE4AVibs/im4j+FsDDhkdfZOY/V9J9AUA/M/8PlnxeAPACAAwPD+85cOBAU/SMjIxgaGioqXfnKkKdZx99IEyDUZvFMouucxFIq/Oj6MPDVMFxnsAHmJlFytqHVr7z/v37DzPzXv1+2xiAL4hoPYC/ZOZdaWn37t3Lhw4daqqcgwcPYt++fU29O1cR6twdCHU24NgZ4OYd4LFNwEPLZo2udqKV70xERgZQlBfQVuXn9wI4UQQdAQEB8xRS9V8OKiAXirIB/AIRbQdQA3AewD8siI6AgID5iC3rgP4+YNnioinpaBTCAJj57xdRbkBAQJegtwJsWls0FR2PIB8FBAQEdCkCAwgICAjoUgQGEBAQENClCAwgICAgoEsRGEBAQEBAlyIwgICAgIAuRWAAAQEBAV2KwAACAgICuhSFxwLKAiK6AbFzuBmsAHAzR3LmAkKduwOhzt2BVuq8nplX6jfnFANoBUR0yBQMaT4j1Lk7EOrcHWhHnYMKKCAgIKBLERhAQEBAQJeimxjAbxZNQAEIde4OhDp3B3Kvc9fYAAICAgIC4ugmCSAgICAgQEFgAAEBAQFdiq5gAET0CSI6SUSniejzRdOTF4jot4noOhEdVe4tI6KvEtGp6O9S5dkXojY4SUTfVQzVzYOIHiGirxHRcSI6RkQ/Gd2fz3XuJ6LXiOhIVOd/Ht2ft3WWIKIyEb1JRF+Jfs/rOhPROSJ6h4jeIqJD0b321pmZ5/U/AGUAZwBsAtAL4AiAHUXTlVPdPg7gaQBHlXu/CODz0fXnAfyr6HpHVPc+ABujNikXXYeM9V0F4OnoeiGA96J6zec6E4Ch6LoC4FsAPjyf66zU/Z8A+LcAvhL9ntd1BnAOwArtXlvr3A0SwLMATjPz+8w8BeAAgO8rmKZcwMxfB3Bbu/19AH43uv5dAH9XuX+AmSeZ+SyA0xBtM2fAzFeZ+Y3o+gGA4wDWYH7XmZl5JPpZif4x5nGdAYCI1gL4TwH8n8rteV1nC9pa525gAGsAXFR+X4ruzVcMM/NVQEyYAB6K7s+rdiCiDQCeglgRz+s6R6qQtwBcB/BVZp73dQbwbwD8DICacm++15kB/A0RHSaiF6J7ba1zIYfCzzLIcK8bfV/nTTsQ0RCAPwXwj5n5PpGpaiKp4d6cqzMzVwE8SURLAHyZiHY5ks/5OhPRdwO4zsyHiWifzyuGe3OqzhE+ysxXiOghAF8lohOOtLnUuRskgEsAHlF+rwVwpSBaZgMfENEqAIj+Xo/uz4t2IKIKxOT/B8z8Z9HteV1nCWa+C+AggE9gftf5owC+l4jOQahsv52Ifh/zu85g5ivR3+sAvgyh0mlrnbuBAbwOYCsRbSSiXgCfAfDvC6apnfj3AD4XXX8OwJ8r9z9DRH1EtBHAVgCvFUBf0yCx1P8tAMeZ+ZeVR/O5ziujlT+IaADAdwA4gXlcZ2b+AjOvZeYNEOP1PzLzD2Me15mIFhDRQnkN4DsBHEW761y05XuWrOufgvAYOQPgi0XTk2O9/hDAVQDTECuCHwOwHMD/B+BU9HeZkv6LURucBPDJoulvor7fBiHmvg3grejfp+Z5nZ8A8GZU56MAfja6P2/rrNV/HxpeQPO2zhBeikeif8fkPNXuOodQEAEBAQFdim5QAQUEBAQEGBAYQEBAQECXIjCAgICAgC5FYAABAQEBXYrAAAICAgK6FIEBBARYQERLiOi/iq5XE9GfFE1TQECeCG6gAQEWRPGGvsLMrtALAQFzFt0QCyggoFn8AoDNUSC2UwAeY+ZdRPSjEFEZywB2AfjXEKHGPwtgEsCnmPk2EW0G8BsAVgIYA/APmNkV3yUgYFYRVEABAXZ8HsAZZn4SwE9rz3YB+EGIeC1fAjDGzE8BeAXAj0RpfhPAP2LmPQB+CsD/MhtEBwT4IkgAAQHN4WssziR4QET3APxFdP8dAE9EEUs/AuCPlWilfbNPZkCAHYEBBAQ0h0nluqb8rkGMqxKAu5H0EBDQkQgqoIAAOx5AHD2ZGcx8H8BZIvo0ICKZEtHuPIkLCGgVgQEEBFjAzLcAfJOIjgL4pSay+CEAP0ZEMsLjvDiKNGD+ILiBBgQEBHQpggQQEBAQ0KUIDCAgICCgSxEYQEBAQECXIjCAgICAgC5FYAABAQEBXYrAAAICAgK6FIEBBAQEBHQp/n/qakEGAMmixwAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# simulate an ARMA(0,1)=MA(1) noise process (moving average of order 1)\n",
-    "mean2 = 0 \n",
-    "sigma2 = 1\n",
-    "theta = 0.7\n",
-    "m = 501\n",
-    "Fs = 1 # sampling rate\n",
-    "\n",
-    "sigma_e = np.sqrt(sigma2**2/(1+theta**2))\n",
-    "yt2 = np.zeros(m) # make an array with zeros\n",
-    "et = np.zeros(m) # make an array with zeros\n",
-    "# initialization of the first entry\n",
-    "et[0] = np.random.randn() * sigma2\n",
-    "yt2[0] = et[0]\n",
-    "# generate values of yt using the MA(1)\n",
-    "for i in range(1, m):\n",
-    "    et[i] = np.random.randn() * sigma_e\n",
-    "    yt2[i] = theta * et[i-1] + et[i]\n",
-    "\n",
-    "# plot the time series\n",
-    "plt.figure()\n",
-    "plt.grid()\n",
-    "plt.plot(time, yt2, color='pink')\n",
-    "plt.title('MA(1) time series')\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('time')\n",
-    "\n",
-    "# plot ACF MA(1) process\n",
-    "ACF = plot_acf(yt2, lags=None, alpha=0.05, title='ACF of MA(1)', color='pink')\n",
-    "plt.grid()\n",
-    "\n",
-    "# plot the MA(1) PSD\n",
-    "F, PSD = signal.periodogram(yt2, fs=Fs, scaling='density', return_onesided=False)\n",
-    "plt.figure()\n",
-    "plt.grid()\n",
-    "plt.plot(F, PSD, color='pink')\n",
-    "plt.title('PSD of MA(1)')\n",
-    "plt.ylabel('Power: PSD')\n",
-    "plt.xlabel('Frequency')\n",
-    "# The PSD values seem to have larger values at lower frequencies. this indicates that lower frequencies \n",
-    "# have higher contribution to data variability (moving avergare reduces the high frequency noise) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b335e79",
-   "metadata": {},
-   "source": [
-    "### Exercise 5. Time series modelling (Video 5)\n",
-    "**Introduction:** In this exercise, you will focus on the Best Linear Unbiased Estimation (BLUE). With BLUE, if the components of the time series are known, you can use the linear model of observations to estimate these components. \n",
-    "\n",
-    "**Exercise:** In this excercise, you calculate the BLUE estimates. First, create your matrix $A$ and $\\Sigma_{Y}$ which need to have dimensions of 501x5 (501: rows and 5 columns) and 501x501 respectively. Can you explain what these 5 parameters are? For $\\Sigma_{Y}$, you can use the np.eye function from numpy. Having defined these two matrices, we can obtain the BLUE estimats of \n",
-    "\n",
-    "$$\n",
-    "\\hat{X}=(A^T \\Sigma_{Y}^{-1}A)^{-1}A^T \\Sigma_{Y}^{-1}Y,\\, \\, \\hat{Y}=...,\\, \\, \\hat{\\epsilon}=...\n",
-    "$$ \n",
-    "\n",
-    "along with their covariance matrices $\\Sigma_{\\hat{X}}=(A^T \\Sigma_{Y}^{-1}A)^{-1}$, $\\Sigma_{\\hat{Y}}=...$ and $\\Sigma_{\\hat{\\epsilon}}=...$. \n",
-    "\n",
-    "After you have estimated the $\\hat{X}$ (having 5 elements), we you can compare each element of the $\\hat{x}$ with the corresponding values from the original time series you simulated ($y_0$, $r$, $A_m$, $\\phi$, $o_k$). The precision of the parameters can also be obtained from $\\Sigma_{\\hat{X}}$. You may also want to follow hypothesis tests to test the statistical significance of the estimated parameters. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "f478f83b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "y0: True value is: 1 ,  Estimated value is: 1.1104794855280757\n",
-      "r: True value is: 0.02 ,  Estimated value is: 0.019749730013193766\n",
-      "Am: True value is: 1 ,  Estimated value is: 0.8989701446874958\n",
-      "phi0: True value is: 0.6283185307179586 ,  Estimated value is: 0.5656440688725001\n",
-      "Ok: True value is: 5 ,  Estimated value is: 4.972202425807366\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# create your code here:\n",
-    "# copied from Exercise 1\n",
-    "m = 501\n",
-    "time = np.arange(501) \n",
-    "y_0 = 1 \n",
-    "r = 0.02 \n",
-    "omega = 2 * np.pi/100 \n",
-    "Am = 1 \n",
-    "phi_0 = 0.2*np.pi \n",
-    "t_k = 300 \n",
-    "sigma = 1\n",
-    "\n",
-    "# for your observations you can make use of y4 (in Exercise 1)\n",
-    "y = y4\n",
-    "# or make a new observation vector based on y3 (signal in Exercise 1) and yt (correlated noise in Exercise 4)\n",
-    "y = y3+yt2\n",
-    "\n",
-    "# the design matrix A based on linear regression + seasonality:\n",
-    "A = np.stack((np.ones(m), time, np.cos(omega*time), np.sin(omega*time)), axis=1)\n",
-    "# include column due to offset into A\n",
-    "u = np.zeros(m)\n",
-    "u[t_k:] = 1\n",
-    "A = np.column_stack((A,u))\n",
-    "# make the covariance matrix\n",
-    "Qyy = (sigma**2) * np.eye(m) \n",
-    "\n",
-    "# implement BLUE Equations:\n",
-    "xhat = np.linalg.inv(A.T @ np.linalg.inv(Qyy) @ A) @ A.T @ np.linalg.inv(Qyy) @ y #BLUE: BLUE of x: xhat\n",
-    "yhat = A @ xhat #BLUE of $Y$: yhat\n",
-    "ehat = y - yhat #BLUE of e: ehat\n",
-    "# covariance matrix of xhat\n",
-    "Qxhat = np.linalg.inv(A.T @ np.linalg.inv(Qyy) @ A)\n",
-    "\n",
-    "# Comparisons of xhat with the initial (true) values x:\n",
-    "y_0_hat = xhat[0] # compare with y_0\n",
-    "print('y0: True value is:', y_0,',  Estimated value is:', y_0_hat)\n",
-    "r_hat = xhat[1]   # compare with r\n",
-    "print('r: True value is:', r,',  Estimated value is:', r_hat)\n",
-    "\n",
-    "Am_hat = np.sqrt(xhat[2]**2 + xhat[3]**2) # compare with Am\n",
-    "print('Am: True value is:', Am,',  Estimated value is:', Am_hat)\n",
-    "\n",
-    "phi_0_hat = np.arctan(xhat[2]/xhat[3]) # compare with phi_0\n",
-    "print('phi0: True value is:', phi_0,',  Estimated value is:', phi_0_hat)\n",
-    "\n",
-    "O_k_hat = xhat[4]  # compare with O_k\n",
-    "print('Ok: True value is:', O_k,',  Estimated value is:', O_k_hat)\n",
-    "\n",
-    "plt.figure(figsize=(10, 5))\n",
-    "plt.grid()\n",
-    "plt.plot(time, y, label='Original $Y$ (with noise)', color='pink')\n",
-    "plt.plot(time, yhat, label='Estimated $Y$: yhat', color='r')\n",
-    "plt.plot(time, y3, label='True $Y$ (without noise)', linestyle='--', color='b')\n",
-    "plt.title('Time series modelling')\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('time')\n",
-    "plt.legend()\n",
-    "\n",
-    "# we now check the residuals to identify the noise structure of ehat. Is it similar to the generated noise? \n",
-    "ACF = plot_acf(ehat, lags=None, alpha=0.05, title='ACF of ehat', color='pink')\n",
-    "plt.grid()\n",
-    "# try to make a new observation vector based on y3 (Exercise 1) and yt (Exercise 4): $Y$ = y3+yt2, and compare the results.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "120c7cb7",
-   "metadata": {},
-   "source": [
-    "### Exercise 6. Time series forecasting (Video 6) - optional\n",
-    "\n",
-    "So far, the $Y$ values are provided for time instants from $t=0$ ($Y(0)$) to $t=500$ ($Y(500)$). The current question pertains to predicting values for $Y$ in the subsequent epochs, such as $Y_p[501]=$? In order to answer this question, we should note that similar to $Y$, $Y_p$ also consists of two components: deterministic (functional) part and stochastic part. For example, for epoch 501, you can make a new design matrix (for now only one row) to calculate the functional component: \n",
-    "\n",
-    "$$\n",
-    "A_p= [1, 501, \\cos(501\\omega), \\sin(501\\omega), 1]\n",
-    "$$\n",
-    "\n",
-    "and \n",
-    "\n",
-    "$$\n",
-    "Y_{p_F} = A_p \\hat{X}\n",
-    "$$\n",
-    "\n",
-    "You need to calculate its stochastic component $Y_{p_S}$. That component depends on the noise process. If the noise process is white noise, there is no contribution from that component. If the noise is MA(1) the stochastic part of the prediction can be obtained from\n",
-    "\n",
-    "$$\n",
-    "Y_{p_S}=Y_{501} = \\theta \\epsilon_{500}+\\epsilon_{501}\n",
-    "$$\n",
-    "\n",
-    "where the error $\\epsilon_{501}$ in epoch 501 is not known, so the best prediction for that would be $\\epsilon_{501}=0$. The error $\\epsilon_{500}$ can be determined from the time series data $\\hat{e}$, and therefore $Y_{501} = \\theta \\epsilon_{500}$. This is not further the subject of discussion/elaboration in this week. In the project, we will provide a Python function to handle/estimate the AR(p) parameters. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "b96e5dd1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Predicted value for epoch 501 is: [16.76202718]\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\AAMIRI~1\\AppData\\Local\\Temp/ipykernel_27972/3448084877.py:29: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
-      "  plt.plot(np.array([500,501]), np.array([y[500], yp]), label='Estimated y: yhat', color='r')\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x2019f166ee0>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from statsmodels.tsa.arima.model import ARIMA\n",
-    "from statsmodels.tsa.arima_process import ArmaProcess\n",
-    "\n",
-    "Ap  = np.array([1, 501, np.cos(omega*501), np.sin(omega*501), 1])\n",
-    "yp1 = Ap@xhat \n",
-    "\n",
-    "plt.figure(figsize=(10, 5))\n",
-    "plt.grid()\n",
-    "plt.plot(time, ehat, label='Least squares residuals', color='pink')\n",
-    "plt.title('Time series modelling')\n",
-    "plt.ylabel('ehat(t)')\n",
-    "plt.xlabel('time')\n",
-    "plt.legend()\n",
-    "\n",
-    "# if you select $Y$ = y3+yt2, containing MA(1)\n",
-    "model = ARIMA(ehat, order=(0, 0, 1))\n",
-    "result = model.fit()\n",
-    "# Forecast future values\n",
-    "forecast_steps = 1\n",
-    "yp2 = result.forecast(steps=forecast_steps)\n",
-    "yp = yp1+yp2\n",
-    "\n",
-    "print('Predicted value for epoch 501 is:', yp)\n",
-    "\n",
-    "plt.figure(figsize=(10, 5))\n",
-    "plt.grid()\n",
-    "plt.plot(time, yp, label='Original $Y$ (with noise)', color='b')\n",
-    "#plt.plot([500,501], [$Y$(500), yp], label='Estimated $Y$: yhat', color='r')\n",
-    "plt.plot(np.array([500,501]), np.array([yp[500], yp]), label='Estimated $Y$: yhat', color='r')\n",
-    "plt.title('Time series modelling')\n",
-    "plt.ylabel('$Y$(t)')\n",
-    "plt.xlabel('time')\n",
-    "plt.legend()\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/book/time_series/optional.md b/book/time_series/optional.md
deleted file mode 100644
index a9eb47fc..00000000
--- a/book/time_series/optional.md
+++ /dev/null
@@ -1,161 +0,0 @@
-(optional)=
-# Supplementary material
-
-```{admonition} MUDE Exam Information
-:class: tip, dropdown
-The material on this page is provided to give you extra insight into time series analysis and how it is used in practice. This material is not part of the exam.
-```
-
-## Partial ACF
-
-A partial ACF (PACF) is a covariance between an observation in a time series with observations at prior time steps with the relationships of intervening observations removed. We work this out using a simple example.
-
-:::{card} Illustration of PACF
-
-Let us assume that we have an autoregressive noise process
-
-$$Y_t = \beta Y_{t-1}+\epsilon_t, \hspace{30px} 0\leq\beta<1, \hspace{30px} t=2,...,m$$
-
-where $\epsilon_t$ is an i.i.d noise process (e.g. distributed as $\epsilon_t\sim N(0,\sigma^2)$). Multiple applications of the above *autoregressive* formula give
-
-$$\begin{align*}
-Y_t&=Y_t\\ 
-Y_{t+1} &= \beta Y_t + \epsilon_{t+1}\\ 
-Y_{t+2}&=\beta Y_{t+1} + \epsilon_{t+2} = \beta^2 Y_t + \beta \epsilon_{t+1} + \epsilon_{t+2}\\ &\vdots \end{align*}$$
-
-We can show that the covariance between $Y_t$ and $Y_{t+1}$ is
-
-$$
-Cov(Y_t, Y_{t+1})  = \sigma^2\beta
-$$
-
-We can also show that the covariance between $Y_t$ and $Y_{t+2}$ is
-
-$$
-Cov(Y_t, Y_{t+2}) =  \sigma^2\beta^2
-$$
-
-Hence, the $Y_t$ and $Y_{t+2}$ are correlated, even though according to the expression $Y_t = \beta Y_{t-1}+\epsilon_t$ should just depend on the previous value. This makes sense, since the previous value again depends on its own previous value, et cetera. However, using the partial ACF allows to 'remove' this correlation.
-
-### Worked example (optional)
-
-Let us now take a look into a worked example on PACF to remove the intervening term, $\beta Y_{t+1}$ between $Y_t$ and $Y_{t+2}$. As we saw before, ACF can be obtained from
-
-$$
-\text{COV} = Cov(Y_t, Y_{t+2}) =  \sigma^2\beta^2
-$$
-
-Regarding the partial ACF, it is knowns from the autoregression $Y_t = \beta Y_{t-1}$ that $\hat{Y}_t = \hat{Y}_{t+2} = \beta Y_{t+1}$. Therefore:
-
-$$
-\begin{align*}
-\text{PCOV} &= Cov(Y_t-\hat{Y}_t,Y_{t+2}-\hat{Y}_{t+2})\\&=Cov(Y_t-\beta Y_{t+1},Y_{t+2}-\beta Y_{t+1})
-\\ & = \mathbb{E}((Y_t-\beta Y_{t+1})(Y_{t+2}-\beta Y_{t+1}))\\
-& = \mathbb{E}(Y_tY_{t+2}-\beta Y_t Y_{t+1}-\beta Y_{t+1}Y_{t+2} + \beta^2Y_{t+1}^2)\\
-& = \sigma^2\beta^2-\beta\sigma^2\beta-\beta\sigma^2\beta+\sigma^2\beta^2=0
-\end{align*}
-$$
-
-This shows indeed that with the partial ACF the correlation for a time lag of 2 (or higher) becomes zero.
-
-#### Normalized ACF vs Partial ACF (optional)
-
-The figure shows a simulated example of $Y_t = 0.8Y_{t-1}+\epsilon_t$ having 1000 samples. The spectrum of the normalized ACF clearly shows that there is a (decreasing) correlation up till lag 10, while the spectrum of the partial ACF only shows correlation at lag 1.
-
-![acf_pacf](./figs/acf_pacf.png "acf_pacf")
-
-:::
-
-## Identifying orders of ARMA process
-
-In time series analysis, [ACF and PACF plots](ACF) can be used to provide the model orders: The value of $p$ for AR and the value of $q$ for MA, and hence to select the best model for forecasting.
-
-Here we assume the time series is stationary. We can then plot the ACF and PACF to identify the orders of AR and MA (numbers $p$ and $q$ of coefficients $\beta_i$ and $\theta_i$) in the ARMA model.
-
-We first look for a gradual diminishing pattern (tail-off pattern) in either ACF or PACF. The following guidelines can then be used to interpret ACF and PACF plots:
-
-* If the **tail-off** pattern is at ACF, then AR (and not MA) is an appropriate model. The cut-off at PACF will then provide order $p$ for AR($p$);
-* If the **tail-off** pattern is at PACF, then MA (and not AR) is an appropriate model. The cut-off at ACF will then provide order $q$ for MA($q$);
-* If the **tail-off** pattern is at both ACF and PACF, then the stochastic process cannot be expressed just as AR or MA. The appropriate model is then ARMA.
-* Test data is usually required to validate the selected orders $p$ and $q$.
-
-### Example: identification of AR(1)
-
-The tail-off pattern is at ACF. AR has a cut-off at PACF at lag 1. The best model is then AR(1) = ARMA($p=1,q=0$)
-
-![pacf_acf](./figs/pacf_acf.png "pacf_acf")
-
-### Example: identification of MA(1)
-
-The tail-off pattern is at PACF. MA has a cut-off at ACF at lag 1. The best model is then MA(1) = ARMA($p=0,q=1$)
-
-![pacf_acf_2](./figs/pacf_acf_2.png "pacf_acf_2")
-
-
-## Testing stationarity
-
-Different tests can be performed to test whether or not a time series is stationary. One of the commonly used methods is the **Augmented Dickey-Fuller (ADF)** test. **ADF** is also optional material. 
-
-Consider a time series
-
-$$Y_t = \beta Y_{t-1}+\epsilon_t$$
-
-where we see that the value at time $t$ depends on the previous value at time $t-1$ plus the noise $\epsilon_t$ (this is an autoregressive process, as we will see later in the section [ARMA process](ARMA)). This implies that if $\beta=1$, the noise is **accumulated** and thus the process is **not stationary**. It is known to be a so-called *random walk noise* process. 
-
-Single differencing gives
-
-$$
-\begin{align*}
-\Delta Y_t = Y_t - Y_{t-1} &= \beta Y_{t-1}+\epsilon_t-Y_{t-1}\\
-&= (\beta - 1)Y_{t-1}+\epsilon_t \\&= \gamma Y_{t-1} + \epsilon_t
-\end{align*}
-$$
-
-The parameter $\gamma = \beta-1$ plays an important role to test the stationarity of the time series.
-
-### ADF test
-
-The ADF test is performed using the following two hypotheses:
-
-* **Null Hypothesis ($\mathcal{H}_0$)**: Time series is non-stationary ($\gamma=0\implies\beta=1$)
-* **Alternative Hypothesis ($\mathcal{H}_a$)**: Time series is stationary ($\gamma<0\implies\beta<1$)
-
-The null hypothesis assumes that the time series consists of non-stationary noise, mainly **Random Walk** noise. Under the alternative hypothesis, the Random Walk noise is absent, and therefore the time series is stationary.
-
-The test statistic is (which can be tested in a given confidence level) given by:
-
-$$
-T_{\text{ADF}}=\frac{\hat{\gamma}}{\sigma_{\hat{\gamma}}}
-$$
-
-The test statistic, $T_{ADF}$ is a **negative number**. The more negative it is, the stronger the rejection of the hypothesis, and hence the more level of confidence that the series is a stationary process.
-
-(BLUP)=
-## Best Linear Unbiased Prediction (BLUP)
-
-Best Linear Unbiased Prediction (BLUP) is the equivalent of Best Linear Unbiased Estimation (BLUE). BLUE can be applied to estimate *deterministic* values, while BLUP is applied in case some of the parameters are of a *random* nature. In case of forecasting, this is indeed the case if the underlying noise process is not white noise.
-
-Consider the (augmented) linear model of observation equations as
-
-$$\begin{bmatrix}Y \\ Y_p\end{bmatrix}=\begin{bmatrix}\mathrm{A} \\ \mathrm{A}_p \end{bmatrix}x+\begin{bmatrix}\epsilon \\ \epsilon_p \end{bmatrix}, \hspace{10px}\mathbb{D}\left(\begin{array}{c}Y\\ Y_p\end{array}\right)=\begin{bmatrix}\Sigma_{Y} & \Sigma_{YY_p} \\\Sigma_{Y_p Y} & \Sigma_{Y_p} \end{bmatrix}$$
-
-The best linear unbiased estimation, **BLUE**, of $x$ is:
-
-$$\hat{X}=(\mathrm{A}^T\Sigma_{Y}^{-1}\mathrm{A})^{-1}\mathrm{A}^T\Sigma_{Y}^{-1}Y,\hspace{10px}\Sigma_{\hat{X}}=(\mathrm{A}^T\Sigma_{Y}^{-1}\mathrm{A})^{-1}$$
-
-Without derivation, we now give the best linear unbiased prediction, **BLUP**, of $Y_p$:
-
-$$\hat{Y_p}=\mathrm{A}_p\hat{X}+\Sigma_{Y_p Y}\Sigma_{Y}^{-1}\hat{\epsilon}= \hat{Y}_F + \hat{S}$$
-
-with the covariance matrix
-
-$$\Sigma_{\hat{Y_p}}=\mathrm{A}_p\Sigma_{\hat{X}}\mathrm{A}_p^T+\Sigma_{Y_p Y}\Sigma_{Y}^{-1}\Sigma_{\hat{\epsilon}}\Sigma_{Y}^{-1}\Sigma_{YY_p}$$
-
-*Two processes play a role in prediction:*
-* $\hat{Y}_F = \mathrm{A}_p\hat{X}$ is the deterministic part modelling the functional effects (such as trend and seasonality).
-* $\hat{S}= \Sigma_{Y_p Y}\Sigma_{Y}^{-1}\hat{\epsilon}$ is the stochastic part (stochastic process).
-
-
-```{note}
-For a purely random process (white noise), we have $\Sigma_{Y_p Y}=0$ and, therefore, the stochastic process/part will NOT affect the prediction.
-```
diff --git a/book/time_series/proof.pdf b/book/time_series/proof.pdf
deleted file mode 100644
index 6fb80f88..00000000
Binary files a/book/time_series/proof.pdf and /dev/null differ
diff --git a/book/time_series/stationarity.md b/book/time_series/stationarity.md
index 7ebcf323..e0cc6ac4 100644
--- a/book/time_series/stationarity.md
+++ b/book/time_series/stationarity.md
@@ -3,7 +3,7 @@
 
 
 ```{admonition} Definition
-A stationary time series is a stochastic process whose statistical properties do not depend on the time at which it is observed.
+A stationary time series $S(t)$ is a stochastic process whose statistical properties do not depend on the time at which it is observed.
 ```
 
 This means that parameters such as *mean* and *(co)variance* should remain constant over time and not follow any trend, seasonality or irregularity.
diff --git a/book/time_series/temp.ipynb b/book/time_series/temp.ipynb
new file mode 100644
index 00000000..d1447255
--- /dev/null
+++ b/book/time_series/temp.ipynb
@@ -0,0 +1,303 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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+       "        7.48151842e-01,  4.43051388e-01,  8.08282554e-01,  4.68694570e-01,\n",
+       "       -9.76716836e-01, -5.65906274e-01, -9.79593683e-01,  9.32812962e-01,\n",
+       "        1.09446918e+00,  1.53156737e+00,  6.07745371e-01,  8.90444322e-01,\n",
+       "        2.58227806e-01,  1.08671023e+00,  1.96989982e-01, -6.81961888e-01,\n",
+       "       -9.03880880e-01, -9.70885459e-01, -2.65810797e-01, -4.29907694e-01,\n",
+       "        4.06336577e-01,  4.78504065e-01,  1.27168476e-01,  1.46771856e+00,\n",
+       "        1.36753502e+00,  4.02762296e-01,  3.64682830e-01, -3.78703131e-01,\n",
+       "        2.96891803e-01, -7.54382156e-01,  1.28006210e+00, -6.86188078e-02,\n",
+       "        1.20052445e+00, -1.66199770e-02,  6.87313607e-01,  7.05341388e-01,\n",
+       "        1.09361043e+00, -5.62887584e-02, -1.02212099e-01, -3.98220516e-01,\n",
+       "       -6.35448516e-01, -1.23454870e+00,  3.30223301e-01,  6.36362445e-01,\n",
+       "        5.95033506e-01, -1.21809241e-01, -1.01669089e+00, -2.83311223e+00,\n",
+       "        7.05074907e-02, -8.14821554e-01, -1.09891995e-01,  8.46936249e-02,\n",
+       "        9.89420750e-01,  1.19584073e-01, -2.55632428e-01,  1.78726604e+00,\n",
+       "        1.15139811e+00,  7.30881522e-01,  3.73337741e-01, -8.84734893e-01,\n",
+       "        9.15512809e-01,  3.48834785e-01, -6.83496242e-01,  1.44386150e+00,\n",
+       "        9.40535741e-01,  1.61809060e+00,  5.90633602e-01, -1.81711114e-01,\n",
+       "       -5.43810530e-01, -9.47633330e-01, -1.08840745e+00, -2.12147676e+00,\n",
+       "       -1.93298030e+00, -1.81721802e+00, -8.60719118e-01, -5.87439117e-01,\n",
+       "       -4.42704828e-01, -7.71683303e-01, -7.09202014e-01, -1.01576221e+00,\n",
+       "       -1.00405087e+00, -2.35322370e+00, -1.55945639e+00, -1.93038555e+00,\n",
+       "       -1.77439000e+00, -7.31110363e-01, -1.11229932e+00, -5.53420963e-01,\n",
+       "       -2.54646158e+00,  1.30171409e-01,  9.58477732e-02, -1.67702799e-01,\n",
+       "        1.07557175e-01,  9.39260555e-01, -1.01729680e+00, -7.51412913e-03,\n",
+       "        2.50457369e-02, -7.93504732e-01,  7.66769888e-02, -4.91396297e-01,\n",
+       "       -6.23168011e-01,  5.56471001e-01, -1.47489436e+00, -1.80051893e-01,\n",
+       "       -9.29699644e-01, -2.86270863e-01, -4.03645146e-01, -1.59207517e+00,\n",
+       "       -1.51789958e+00, -5.17777627e-01, -1.44915841e+00, -2.82704891e-01,\n",
+       "       -7.77895523e-01,  4.24394846e-02,  6.39871136e-01, -8.25068603e-01,\n",
+       "        1.70283470e-01, -3.04672894e-01,  7.57377629e-01,  2.81579728e-01,\n",
+       "        9.00245371e-01,  4.38308567e-01,  6.62981478e-01,  2.26340506e-01,\n",
+       "        6.55124877e-01, -4.74574465e-02,  5.83536959e-01,  3.30224150e-01,\n",
+       "        1.74725260e+00,  2.92914951e-01,  6.53733549e-01,  1.90472489e+00,\n",
+       "        2.13062770e+00,  8.29887814e-01,  1.78665718e+00,  3.36002901e+00,\n",
+       "        1.80680569e+00,  7.24783016e-01, -8.31104542e-02,  1.06971812e+00,\n",
+       "       -7.92409025e-01, -1.12136978e-02,  1.36246834e-01, -3.96155791e-01,\n",
+       "       -5.17756929e-02, -5.53090799e-01,  1.27895232e-01, -1.14333497e-01,\n",
+       "        5.69661453e-02, -1.30418263e+00, -4.20354967e-01, -9.69997976e-02,\n",
+       "        1.25838428e+00,  7.67147245e-01, -1.96356797e-01, -7.25875722e-01,\n",
+       "        9.13773378e-03, -1.92841740e-01,  1.36680690e+00,  7.85367122e-02,\n",
+       "        1.43978042e+00,  3.53036371e-01,  3.21726380e-01,  7.27328771e-01,\n",
+       "        3.42772844e-01, -8.08014869e-01, -7.84040601e-01, -9.71464217e-01,\n",
+       "       -3.38824750e-01, -1.28809967e+00, -3.87057040e-01, -9.72010741e-01,\n",
+       "       -1.39459163e+00, -5.41007591e-01, -1.38221603e+00, -1.88021763e+00,\n",
+       "       -1.25018544e+00, -8.95854470e-01, -2.42330929e+00, -1.93677650e+00,\n",
+       "       -2.85864261e+00, -2.28733387e+00, -3.12822402e+00, -2.65251704e+00,\n",
+       "       -3.18254573e+00, -2.43695779e+00, -3.33016093e+00, -2.56150214e+00,\n",
+       "       -2.40580874e+00, -2.04801271e+00, -9.45886359e-01, -4.80283888e-01,\n",
+       "       -9.02308417e-01,  4.39017348e-02,  1.40193351e+00,  1.12539513e-04,\n",
+       "        1.20803285e+00, -1.05656599e-01, -8.15697707e-01, -1.33205596e+00,\n",
+       "       -1.11363610e+00, -1.55571463e+00, -2.35843487e+00, -3.80555093e-01,\n",
+       "       -1.42667129e-01,  1.87343826e-01, -9.03622737e-01,  1.58042120e+00,\n",
+       "        9.14429754e-01,  1.43619930e+00,  1.91061472e+00,  9.74028580e-01,\n",
+       "        5.34409977e-01, -1.90812107e-01,  1.31103428e+00,  2.35584323e+00,\n",
+       "        1.02766574e+00,  4.17273584e-01,  1.34848289e+00,  1.26365466e+00,\n",
+       "        1.24666532e-02,  2.31648529e-02, -2.83896716e-01,  3.74971332e-01,\n",
+       "        1.59599098e-01,  6.12558213e-01, -8.27835045e-02,  7.12749888e-01,\n",
+       "        1.34679669e+00,  1.12912214e+00,  5.15484989e-01,  6.28624387e-01,\n",
+       "       -2.45869042e-01,  3.27209328e-01, -4.96825605e-01,  1.31898972e-01,\n",
+       "       -1.81544836e-01,  5.41585156e-01,  8.66005696e-01, -1.34887158e+00,\n",
+       "       -1.05298192e+00, -3.55200747e-02, -6.17742426e-02,  3.15934090e-01,\n",
+       "        7.58516075e-01, -1.28638664e-01, -3.62368353e-01,  1.85925648e-01,\n",
+       "        1.10367660e+00, -4.71440744e-02,  1.15255335e+00,  9.62837157e-01,\n",
+       "        1.01530785e+00,  5.16531675e-01,  5.18742934e-01,  7.35507772e-01,\n",
+       "        3.67891288e-01, -9.15120567e-01, -7.12922515e-01,  7.95935851e-01,\n",
+       "       -8.24176529e-01,  1.01923996e-01, -7.19323604e-01, -8.11150057e-01,\n",
+       "        2.11116100e-01,  7.08622195e-01,  1.20049183e+00,  1.07098285e+00,\n",
+       "        3.68214610e-01,  3.54257026e-01,  7.05818294e-02, -3.39895972e-01,\n",
+       "       -1.16645409e-01,  1.82229015e-01, -7.70677164e-01, -9.31566188e-01,\n",
+       "        1.47087451e-01,  1.06149338e+00,  1.15550485e+00,  8.00273717e-01,\n",
+       "        1.55176802e+00,  2.10226314e+00,  3.95218245e-01,  2.77919345e-01,\n",
+       "        1.46034669e+00,  1.19407058e+00,  1.95311543e+00,  1.77257448e+00,\n",
+       "        4.11952827e-01, -1.03775982e-01,  1.60578453e-01, -5.78448463e-01,\n",
+       "       -6.74402250e-02, -5.38429388e-02, -9.56971433e-01, -1.77622550e-01,\n",
+       "       -2.58961602e-01,  6.06878724e-01, -2.61909649e-01, -3.05580909e-01,\n",
+       "       -6.47796264e-01, -1.38091098e+00, -8.81668249e-01, -4.40749857e-01,\n",
+       "       -9.64560692e-01, -9.02942803e-01, -1.51032638e+00, -9.78485228e-01,\n",
+       "       -1.35448247e+00, -5.20396184e-01, -1.63593472e-01,  1.39960962e-01,\n",
+       "        7.61197724e-01, -1.80842551e-01, -6.66113910e-01, -2.03078431e-02,\n",
+       "        4.59656603e-02, -6.24886605e-01, -7.55849348e-01, -2.62076342e-01,\n",
+       "        1.10954752e-01, -9.38955437e-01, -3.39143906e-01, -8.10682908e-01,\n",
+       "        4.23887208e-01,  8.61515571e-01,  6.65772041e-01,  2.31012501e+00,\n",
+       "        2.24641806e+00,  2.03232659e+00,  2.50357257e+00,  2.28339898e+00,\n",
+       "        1.95069044e+00,  1.63523854e+00,  1.37286544e+00,  2.43219243e+00,\n",
+       "        2.55092337e+00,  1.76595226e+00, -8.62412017e-02,  1.59579762e+00,\n",
+       "        5.08483834e-01,  4.15151476e-01,  6.93189435e-01,  9.21821403e-01,\n",
+       "        1.29025111e+00,  7.93774884e-02,  6.80210192e-02, -6.94808049e-01,\n",
+       "       -3.51609274e-01,  1.47062707e+00,  5.89348866e-01,  2.11627876e+00,\n",
+       "        6.52185541e-01,  1.26132338e+00,  6.98691324e-01,  3.48855150e-01,\n",
+       "       -1.33109062e+00, -1.47342324e+00,  1.09013417e+00, -1.11974953e-02,\n",
+       "        4.17028815e-01,  3.76022584e-01,  2.70449474e-01,  5.42963217e-01,\n",
+       "       -2.79830499e-01, -4.99062723e-01,  1.69929223e-01, -6.13573591e-01,\n",
+       "       -7.45367870e-01, -9.69028857e-01, -2.33629353e+00, -2.03188486e+00,\n",
+       "       -1.81089730e+00, -1.49790534e+00, -1.99281756e+00, -1.81819238e+00,\n",
+       "       -1.47388646e+00, -1.81642763e+00, -2.32034092e+00, -9.46747332e-01,\n",
+       "       -6.22658430e-01, -6.70254068e-01, -1.25268470e+00, -5.51126224e-01,\n",
+       "        1.12351647e+00,  7.41654766e-01, -4.56727439e-01, -5.36036952e-01,\n",
+       "        3.79299501e-01,  6.10543198e-01,  1.04647975e+00, -5.26770962e-02,\n",
+       "        9.10931820e-01,  2.88667270e-01,  1.43372970e+00,  8.83455823e-01,\n",
+       "        7.40768747e-01,  1.02984778e+00, -1.46106980e-01,  3.70249466e-01,\n",
+       "       -5.48979579e-01, -1.64568153e+00,  3.48798867e-01,  2.17017707e-01,\n",
+       "       -1.37474632e-01,  9.53455170e-01, -7.06646698e-01, -7.85727930e-01,\n",
+       "        3.17707117e-01, -7.46688323e-01,  5.72143464e-01,  1.22252222e+00,\n",
+       "        6.49886478e-01,  1.11199626e+00,  2.98352821e+00,  2.47769388e+00,\n",
+       "        1.18576263e+00,  5.33805666e-01,  2.10096587e+00,  1.59574729e+00,\n",
+       "        1.31832487e+00,  3.84267233e-01,  1.18968620e+00,  2.39827315e+00,\n",
+       "        1.53984467e+00,  1.73049810e+00,  3.03364076e-01, -7.57727641e-01,\n",
+       "       -6.75717011e-01, -1.05885226e+00, -7.91154756e-01,  2.93195048e-01,\n",
+       "       -8.55754313e-01, -2.03581616e-01, -4.70712707e-01,  4.37962580e-01,\n",
+       "        8.03218956e-01,  5.72756250e-01, -2.09349498e-01,  6.93622013e-01,\n",
+       "        1.52325261e+00,  7.46335450e-01,  1.04179128e+00,  6.93180243e-01,\n",
+       "        6.15903795e-01,  1.72977174e+00, -8.97999867e-02,  2.50893420e+00,\n",
+       "        1.78139472e+00,  1.30976033e+00,  5.56591118e-01,  2.35011371e-01,\n",
+       "       -3.85188166e-01, -4.47569045e-01, -1.24490106e-02, -3.24913641e-02,\n",
+       "        7.26347917e-01, -7.93962450e-01, -1.13628472e+00, -3.70710503e-01,\n",
+       "       -1.41297811e+00, -7.05695681e-01, -6.89106682e-01, -8.76022961e-01,\n",
+       "       -6.35076122e-01, -2.20968410e+00, -1.66157685e+00, -2.78915712e-01,\n",
+       "       -7.35596283e-01, -7.08503954e-01,  1.98219493e-01, -1.47695316e+00,\n",
+       "       -4.35808912e-01, -1.13217275e-01, -1.08390134e+00,  8.56553143e-01,\n",
+       "        5.19019577e-01,  4.48514228e-01, -1.49052322e-01,  1.64228141e-01,\n",
+       "       -2.38744808e-02, -9.94880817e-01, -1.09619291e+00, -1.23149681e+00,\n",
+       "       -2.29443615e+00, -9.24490557e-01, -1.41361200e+00, -1.14823308e+00,\n",
+       "        6.35477621e-01,  5.99331444e-01, -5.51869204e-01,  4.57836691e-01,\n",
+       "        1.36644386e+00,  1.99158100e+00,  1.05917265e+00, -6.20214682e-01,\n",
+       "       -3.03986356e-01,  9.21210441e-01,  1.86984440e+00,  7.19809989e-01,\n",
+       "        2.24614850e-01,  1.83986293e+00, -4.98484113e-01,  8.48969414e-01,\n",
+       "        1.46145990e+00,  4.64898199e-01,  5.95667867e-01, -1.65015303e-01,\n",
+       "        4.98926356e-01,  1.27696976e+00,  6.31788684e-01,  9.02383220e-01,\n",
+       "        9.09245114e-02, -8.33510548e-01, -7.73376882e-01,  4.20514784e-01,\n",
+       "        4.00816394e-01,  4.23429604e-01,  7.03190294e-01,  2.45607983e-01,\n",
+       "        1.12766363e-01,  2.29990495e-01,  1.44303637e-01,  2.98763762e-01,\n",
+       "        1.66542808e+00,  2.09103268e+00,  1.90449043e+00,  1.72099303e+00,\n",
+       "        1.25933300e+00,  2.29352112e+00,  1.97652899e-01,  1.17981554e+00,\n",
+       "       -1.11019238e+00, -3.31291763e-01, -4.81943208e-01, -1.57380538e+00,\n",
+       "       -8.01543386e-01, -1.57373293e+00, -6.11527712e-01, -1.17852491e+00,\n",
+       "        4.34070610e-01, -9.47518014e-01, -1.21053098e+00, -8.52856246e-01,\n",
+       "       -1.25192446e+00, -1.30987448e-01,  2.71523161e-01, -1.02593613e+00,\n",
+       "        4.19178470e-01, -3.32333953e-01, -7.02741725e-01,  1.18590220e+00])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.genfromtxt('generated_time_series.csv', delimiter=',')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "TAMude",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/book/time_series/videos.md b/book/time_series/videos.md
deleted file mode 100644
index 81a1a9f2..00000000
--- a/book/time_series/videos.md
+++ /dev/null
@@ -1,94 +0,0 @@
-# Supplementary Videos
-
-**MMMMM:** _these videos are from 2022; decide which videos to keep for 2023, and (optionally) integrate them into the respective pages. Update text and dropdown as needed._
-
-_Codes for the videos:_  
-
-```bash
-2.5.1a, components of TS: https://youtu.be/_euBY6-CK54
-2.5.1a, worked example: https://youtu.be/8kqQiI4ni68
-2.5.1b, stationary TS: https://youtu.be/MOTNftkPvbw
-2.5.1b, worked example: https://youtu.be/hZSqlXjUg0k
-2.5.1c, autocov fxn: https://youtu.be/wdlGzV5zr-w
-2.5.1c, worked example (1 of 2, 4:46 long): https://youtu.be/SoCKuF2eiTc
-2.5.1c, worked example (2 of 2, 4:11 long): https://youtu.be/n-pkyuL95JA
-2.5.1d, ARMA process: https://youtu.be/v7odPKK_eN4
-2.5.2a, TSA model and est: https://youtu.be/6iJ1kOnl7is
-2.5.2, TS forecasting: https://youtu.be/0KEX-Z_lCTM
-```
-
-The story of this chapter is told once more in this section with a series of videos. 
-
-```{admonition} MUDE exam information 
-:class: tip, dropdown
-These videos overlap to some extent with the theory presented in the book, and are meant to provide additional perspective on the same topic. Additional material that is presented in these videos is _not_ part of the exam; in other words, the exam scope is limited  to contents that appear in the previous pages.
-```
-
-```{eval-rst}
-.. raw:: html
-
-    <iframe width="560" height="315" src="https://www.youtube.com/embed/_euBY6-CK54" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-```
-
-
-```{eval-rst}
-.. raw:: html
-
-    <iframe width="560" height="315" src="https://www.youtube.com/embed/8kqQiI4ni68" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-```
-
-
-```{eval-rst}
-.. raw:: html
-
-    <iframe width="560" height="315" src="https://www.youtube.com/embed/MOTNftkPvbw" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-```
-
-
-```{eval-rst}
-.. raw:: html
-
-    <iframe width="560" height="315" src="https://www.youtube.com/embed/hZSqlXjUg0k" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-```
-
-
-```{eval-rst}
-.. raw:: html
-
-    <iframe width="560" height="315" src="https://www.youtube.com/embed/wdlGzV5zr-w" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-```
-
-
-```{eval-rst}
-.. raw:: html
-
-    <iframe width="560" height="315" src="https://www.youtube.com/embed/SoCKuF2eiTc" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-```
-
-
-```{eval-rst}
-.. raw:: html
-
-    <iframe width="560" height="315" src="https://www.youtube.com/embed/n-pkyuL95JA" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-```
-
-
-```{eval-rst}
-.. raw:: html
-
-    <iframe width="560" height="315" src="https://www.youtube.com/embed/v7odPKK_eN4" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-```
-
-
-```{eval-rst}
-.. raw:: html
-
-    <iframe width="560" height="315" src="https://www.youtube.com/embed/6iJ1kOnl7is" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-```
-
-
-```{eval-rst}
-.. raw:: html
-
-    <iframe width="560" height="315" src="https://www.youtube.com/embed/0KEX-Z_lCTM" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-```