DLM_Codes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import invgamma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First-Order Polynomial Constant Model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observational and evolution variances (W and V) are constant over time. Univariate time series with no intercept.\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "<center> <b>Observation Equation</b><br>\n",
    "   <br>\n",
    "   Y_{t} = Q_{t} + V where,  V ~ N[0, v_{t}]    <br>\n",
    "    \n",
    "    \n",
    " <br><center><b>State Equation </b><br>\n",
    "   \n",
    "   <br>\n",
    "   Q_{t} = Q_{t-1} + W where, W ~ N[0, v_{t})]    \n",
    "   <br>\n",
    "    \n",
    " <br><center><b>Initial Conditions </b> <br>\n",
    "    <br>\n",
    "    Q0 ~ N[m_{0}, C_{0}] </center></center></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example excercise from West & Harrison to start with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Yt = [150, 136, 143, 154, 135, 148, 128, 149, 146]\n",
    "m0 = 130\n",
    "C0 = 400\n",
    "V = 100\n",
    "W = 5\n",
    "df = pd.DataFrame(columns = ['Yt', 'mt', 'Ct', 'ft', 'Qt', 'At', 'et'])\n",
    "\n",
    "time_index = 0\n",
    "m_t = None\n",
    "c_t = None\n",
    "\n",
    "\n",
    "for Y in Yt:\n",
    "    if time_index == 0:\n",
    "        m_pt = m0\n",
    "        c_pt = C0\n",
    "        time_index += 1\n",
    "    else:\n",
    "        m_pt = m_t\n",
    "        c_pt = c_t\n",
    "        time_index += 1\n",
    "        \n",
    "    f_t = m_pt\n",
    "    R_t = c_pt + W\n",
    "    Q_t = R_t + V\n",
    "    A_t = R_t/Q_t\n",
    "    e_t = Y-f_t \n",
    "    c_t = (A_t*V)\n",
    "    m_t = f_t + ((A_t*e_t))\n",
    "    df = df.append({'Yt': Y,\n",
    "                    'mt': m_t,\n",
    "                    'ft':f_t,\n",
    "                    'Ct': c_t,\n",
    "                    'Qt': Q_t,\n",
    "                    'At': A_t,\n",
    "                    'et': e_t\n",
    "                   }, ignore_index=True)\n",
    "    \n",
    "df.round(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Air Passanger DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Month  Passengers\n",
      "0  1949-01         112\n",
      "1  1949-02         118\n",
      "2  1949-03         132\n",
      "3  1949-04         129\n",
      "4  1949-05         121\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"AirPassengers.csv\")\n",
    "df['Month']= pd.to_datetime(df['Month'], format='%Y-%m').dt.strftime('%Y-%m')\n",
    "df['Passengers'] = df['#Passengers']\n",
    "df.drop(['#Passengers'], axis=1, inplace= True)\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forward Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8813c23850>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2880x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Yt = df['Passengers']\n",
    "m0 = 110\n",
    "C0 = 200\n",
    "# V & W will be also a random draw normal - inverse gamma for both. \n",
    "V = 5\n",
    "W = 5\n",
    "ff_df = pd.DataFrame(columns = ['Yt', 'mt', 'Ct', 'ft', 'Qt', 'At', 'et', 'Rt'])\n",
    "\n",
    "time_index = 0\n",
    "m_t = None\n",
    "c_t = None\n",
    "\n",
    "\n",
    "for Y in Yt:\n",
    "    if time_index == 0:\n",
    "        m_pt = m0\n",
    "        c_pt = C0\n",
    "        time_index += 1\n",
    "    else:\n",
    "        m_pt = m_t \n",
    "        c_pt = c_t\n",
    "        time_index += 1\n",
    "        \n",
    "    f_t = m_pt\n",
    "    R_t = c_pt + W\n",
    "    Q_t = R_t + V\n",
    "    A_t = R_t/Q_t\n",
    "    e_t = Y - f_t \n",
    "    c_t = (A_t*V)\n",
    "    m_t = f_t + ((A_t*e_t))\n",
    "    ff_df = ff_df.append({'Yt': Y,\n",
    "                    'mt': m_t,\n",
    "                    'ft':f_t,\n",
    "                    'Ct': c_t,\n",
    "                    'Qt': Q_t,\n",
    "                    'At': A_t,\n",
    "                    'et': e_t,\n",
    "                    'Rt': R_t\n",
    "                        \n",
    "                   }, ignore_index=True)\n",
    "    \n",
    "ff_df\n",
    "Yt.plot.line(figsize=(40,10), label='Yt')\n",
    "# plt.plot(ff_df['mt'], label = 'mt')\n",
    "# plt.legend()\n",
    "# plt.show()\n",
    "\n",
    "ff_df.plot.line(figsize=(10,10))\n",
    "plt.legend(loc=2, prop={'size': 15})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Backward Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>113.838377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>111.732502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>123.812609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>125.056096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>125.374219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>139</td>\n",
       "      <td>596.950362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>140</td>\n",
       "      <td>541.846727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>141</td>\n",
       "      <td>493.134461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>142</td>\n",
       "      <td>431.599105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>143</td>\n",
       "      <td>429.571894</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0\n",
       "0    113.838377\n",
       "1    111.732502\n",
       "2    123.812609\n",
       "3    125.056096\n",
       "4    125.374219\n",
       "..          ...\n",
       "139  596.950362\n",
       "140  541.846727\n",
       "141  493.134461\n",
       "142  431.599105\n",
       "143  429.571894\n",
       "\n",
       "[144 rows x 1 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_T = ff_df.iloc[-1]['mt']\n",
    "c_T = ff_df.iloc[-1]['Ct']\n",
    "theta_T = np.random.normal(m_T, c_T)\n",
    "\n",
    "thetas = pd.DataFrame(columns = ['Theta'])\n",
    "\n",
    "for i in range(len(ff_df['Yt'])-1,-1,-1):\n",
    "    if i == len(ff_df['Yt'])-1:\n",
    "        theta_nt = theta_T\n",
    "        #print(i, round(theta_nt,1))\n",
    "        \n",
    "    else:\n",
    "#         print()\n",
    "#         print(i, round(theta_nt, 1))\n",
    "        B_t = (ff_df.iloc[i]['Ct']/ff_df.iloc[i+1]['Rt'])\n",
    "        \n",
    "# Drawing theta from theta_nt\n",
    "        g_t = ff_df.iloc[i]['mt'] + B_t*(theta_nt - ff_df.iloc[i+1]['mt'])\n",
    "        H_t = ff_df.iloc[i]['Ct'] - (B_t*ff_df.iloc[i+1]['Rt']*B_t)\n",
    "        theta_nt = np.random.normal(g_t, H_t)\n",
    "    thetas = thetas.append({'Theta': theta_nt}, ignore_index= True)\n",
    "\n",
    "a = []\n",
    "for i in range(len(thetas['Theta'])-1,-1,-1):\n",
    "    a.append(thetas['Theta'][i])\n",
    "\n",
    "a = pd.DataFrame(a)\n",
    "a\n",
    "\n",
    "# Yt.plot.line(figsize=(30,10), label='Yt')\n",
    "# plt.plot(a, label = 'Thetas')\n",
    "# plt.legend(loc=2, prop={'size': 30})\n",
    "# plt.show()\n",
    "\n",
    "# # Draw V & W "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Way Forward"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Setup Gibbs Sampling with Burn in periods (Carter & Kohn Paper) <br>\n",
    "- Extend model to Multivariate Time Series with Matrix for Parameters, Variances, etc. <br> \n",
    "- Hierarchical models  <br>\n",
    "- Add other features such as seasonality, etc.  <br>\n",
    "- Optimize Code for large datasets (DASK) <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_T = ff_df.iloc[-1]['mt']\n",
    "c_T = ff_df.iloc[-1]['Ct']\n",
    "theta_T = np.random.normal(m_T, c_T)\n",
    "\n",
    "thetas = pd.DataFrame(columns = ['Theta'])\n",
    "\n",
    "for i in range(len(ff_df['Yt'])-1,-1,-1):\n",
    "    if i == len(ff_df['Yt'])-1:\n",
    "        theta_nt = theta_T\n",
    "        #print(i, round(theta_nt,1))\n",
    "        \n",
    "    else:\n",
    "#         print()\n",
    "#         print(i, round(theta_nt, 1))\n",
    "        B_t = (ff_df.iloc[i]['Ct']/ff_df.iloc[i+1]['Rt'])\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yt59</th>\n",
       "      <th>mt59</th>\n",
       "      <th>Ct59</th>\n",
       "      <th>ft59</th>\n",
       "      <th>Qt59</th>\n",
       "      <th>At59</th>\n",
       "      <th>et59</th>\n",
       "      <th>Rt59</th>\n",
       "      <th>Vt59</th>\n",
       "      <th>Wt59</th>\n",
       "      <th>Theta59</th>\n",
       "      <th>E_Y_Theta_Sqr59</th>\n",
       "      <th>E_Theta_Theta_Sqr59</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>111.977246</td>\n",
       "      <td>0.004462</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.004469</td>\n",
       "      <td>0.999777</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>20.000007</td>\n",
       "      <td>0.004463</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>111.977268</td>\n",
       "      <td>0.000517</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>118.0</td>\n",
       "      <td>114.990612</td>\n",
       "      <td>0.002233</td>\n",
       "      <td>111.977246</td>\n",
       "      <td>0.008931</td>\n",
       "      <td>0.500330</td>\n",
       "      <td>6.022754</td>\n",
       "      <td>0.004468</td>\n",
       "      <td>0.004463</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>114.990639</td>\n",
       "      <td>9.056257</td>\n",
       "      <td>9.080404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>132.0</td>\n",
       "      <td>120.674565</td>\n",
       "      <td>0.001491</td>\n",
       "      <td>114.990612</td>\n",
       "      <td>0.006702</td>\n",
       "      <td>0.334166</td>\n",
       "      <td>17.009388</td>\n",
       "      <td>0.002240</td>\n",
       "      <td>0.004463</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>120.674582</td>\n",
       "      <td>128.265091</td>\n",
       "      <td>32.307215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>129.0</td>\n",
       "      <td>122.767036</td>\n",
       "      <td>0.001122</td>\n",
       "      <td>120.674565</td>\n",
       "      <td>0.005961</td>\n",
       "      <td>0.251335</td>\n",
       "      <td>8.325435</td>\n",
       "      <td>0.001498</td>\n",
       "      <td>0.004463</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>122.767037</td>\n",
       "      <td>38.849828</td>\n",
       "      <td>4.378367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>121.0</td>\n",
       "      <td>122.410380</td>\n",
       "      <td>0.000901</td>\n",
       "      <td>122.767036</td>\n",
       "      <td>0.005591</td>\n",
       "      <td>0.201839</td>\n",
       "      <td>-1.767036</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0.004463</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>122.410380</td>\n",
       "      <td>1.989170</td>\n",
       "      <td>0.127205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>139</td>\n",
       "      <td>606.0</td>\n",
       "      <td>405.559420</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>397.524727</td>\n",
       "      <td>0.004641</td>\n",
       "      <td>0.038540</td>\n",
       "      <td>208.475273</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>0.004463</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>405.559631</td>\n",
       "      <td>40176.341516</td>\n",
       "      <td>64.556375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>140</td>\n",
       "      <td>508.0</td>\n",
       "      <td>409.507497</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>405.559420</td>\n",
       "      <td>0.004641</td>\n",
       "      <td>0.038540</td>\n",
       "      <td>102.440580</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>0.004463</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>409.507725</td>\n",
       "      <td>9700.728194</td>\n",
       "      <td>15.587448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>141</td>\n",
       "      <td>461.0</td>\n",
       "      <td>411.492022</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>409.507497</td>\n",
       "      <td>0.004641</td>\n",
       "      <td>0.038540</td>\n",
       "      <td>51.492503</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>0.004463</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>411.492253</td>\n",
       "      <td>2451.016974</td>\n",
       "      <td>3.938352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>142</td>\n",
       "      <td>390.0</td>\n",
       "      <td>410.663720</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>411.492022</td>\n",
       "      <td>0.004641</td>\n",
       "      <td>0.038540</td>\n",
       "      <td>-21.492022</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>0.004463</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>410.663968</td>\n",
       "      <td>426.999587</td>\n",
       "      <td>0.686056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>143</td>\n",
       "      <td>432.0</td>\n",
       "      <td>411.486018</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>410.663720</td>\n",
       "      <td>0.004641</td>\n",
       "      <td>0.038540</td>\n",
       "      <td>21.336280</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>0.004463</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>411.486282</td>\n",
       "      <td>420.812621</td>\n",
       "      <td>0.676200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Yt59        mt59      Ct59        ft59       Qt59      At59        et59  \\\n",
       "0    112.0  111.977246  0.004462   10.000000  20.004469  0.999777  102.000000   \n",
       "1    118.0  114.990612  0.002233  111.977246   0.008931  0.500330    6.022754   \n",
       "2    132.0  120.674565  0.001491  114.990612   0.006702  0.334166   17.009388   \n",
       "3    129.0  122.767036  0.001122  120.674565   0.005961  0.251335    8.325435   \n",
       "4    121.0  122.410380  0.000901  122.767036   0.005591  0.201839   -1.767036   \n",
       "..     ...         ...       ...         ...        ...       ...         ...   \n",
       "139  606.0  405.559420  0.000172  397.524727   0.004641  0.038540  208.475273   \n",
       "140  508.0  409.507497  0.000172  405.559420   0.004641  0.038540  102.440580   \n",
       "141  461.0  411.492022  0.000172  409.507497   0.004641  0.038540   51.492503   \n",
       "142  390.0  410.663720  0.000172  411.492022   0.004641  0.038540  -21.492022   \n",
       "143  432.0  411.486018  0.000172  410.663720   0.004641  0.038540   21.336280   \n",
       "\n",
       "          Rt59      Vt59      Wt59     Theta59  E_Y_Theta_Sqr59  \\\n",
       "0    20.000007  0.004463  0.000007  111.977268         0.000517   \n",
       "1     0.004468  0.004463  0.000007  114.990639         9.056257   \n",
       "2     0.002240  0.004463  0.000007  120.674582       128.265091   \n",
       "3     0.001498  0.004463  0.000007  122.767037        38.849828   \n",
       "4     0.001128  0.004463  0.000007  122.410380         1.989170   \n",
       "..         ...       ...       ...         ...              ...   \n",
       "139   0.000179  0.004463  0.000007  405.559631     40176.341516   \n",
       "140   0.000179  0.004463  0.000007  409.507725      9700.728194   \n",
       "141   0.000179  0.004463  0.000007  411.492253      2451.016974   \n",
       "142   0.000179  0.004463  0.000007  410.663968       426.999587   \n",
       "143   0.000179  0.004463  0.000007  411.486282       420.812621   \n",
       "\n",
       "     E_Theta_Theta_Sqr59  \n",
       "0                    NaN  \n",
       "1               9.080404  \n",
       "2              32.307215  \n",
       "3               4.378367  \n",
       "4               0.127205  \n",
       "..                   ...  \n",
       "139            64.556375  \n",
       "140            15.587448  \n",
       "141             3.938352  \n",
       "142             0.686056  \n",
       "143             0.676200  \n",
       "\n",
       "[144 rows x 13 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Yt = df['Passengers']\n",
    "df_list = list()\n",
    "\n",
    "for draw in range(60):\n",
    "    time_index = 0\n",
    "    m_t = None\n",
    "    c_t = None\n",
    "    m0 = 10\n",
    "    C0 = 20\n",
    "    E_Y_Theta_Sqr_Sum = 0 \n",
    "    E_Theta_Theta_Sqr_Sum = 0\n",
    "    \n",
    "    if draw  == 0:\n",
    "        V = 1\n",
    "        W = 1\n",
    "   \n",
    "    else:\n",
    "        V = invgamma.rvs((1+len(Yt)),scale=(0.6+E_Y_Theta_Sqr_Sum))\n",
    "        W = invgamma.rvs((1+len(Yt)-1)/2,scale=(0.001+E_Theta_Theta_Sqr_Sum)/2)\n",
    "        \n",
    "    ff_df = pd.DataFrame(columns = ['Yt', 'mt', 'Ct', 'ft', 'Qt', 'At', 'et', 'Rt'])\n",
    "    \n",
    "    for Y in Yt:\n",
    "        if time_index == 0:\n",
    "            m_pt = m0\n",
    "            c_pt = C0\n",
    "            time_index += 1\n",
    "        else:\n",
    "            m_pt = m_t \n",
    "            c_pt = c_t\n",
    "            time_index += 1\n",
    "\n",
    "        f_t = m_pt\n",
    "        V_t = V\n",
    "        W_t = W\n",
    "        R_t = c_pt + W\n",
    "        Q_t = R_t + V\n",
    "        A_t = R_t/Q_t\n",
    "        e_t = Y - f_t \n",
    "        c_t = (A_t*V)\n",
    "        m_t = f_t + ((A_t*e_t))\n",
    "        ff_df = ff_df.append({'Yt': Y,\n",
    "                        'mt': m_t,\n",
    "                        'ft':f_t,\n",
    "                        'Ct': c_t,\n",
    "                        'Qt': Q_t,\n",
    "                        'At': A_t,\n",
    "                        'et': e_t,\n",
    "                        'Rt': R_t,\n",
    "                        'Vt': V_t,\n",
    "                        'Wt': W_t\n",
    "\n",
    "                       }, ignore_index=True)\n",
    "\n",
    "\n",
    "    # Yt.plot.line(figsize=(40,10), label='Yt')\n",
    "    # # plt.plot(ff_df['mt'], label = 'mt')\n",
    "    # # plt.legend()\n",
    "    # # plt.show()\n",
    "\n",
    "    # ff_df.plot.line(figsize=(10,10))\n",
    "    # plt.legend(loc=2, prop={'size': 15})\n",
    "\n",
    "    m_T = ff_df.iloc[-1]['mt']\n",
    "    c_T = ff_df.iloc[-1]['Ct']\n",
    "    theta_T = np.random.normal(m_T, c_T)\n",
    "\n",
    "    thetas = pd.DataFrame(columns = ['Theta'])\n",
    "\n",
    "    for i in range(len(ff_df['Yt'])-1,-1,-1):\n",
    "        if i == len(ff_df['Yt'])-1:\n",
    "            theta_nt = theta_T\n",
    "            #print(i, round(theta_nt,1))\n",
    "\n",
    "        else:\n",
    "    #         print()\n",
    "    #         print(i, round(theta_nt, 1))\n",
    "            B_t = (ff_df.iloc[i]['Ct']/ff_df.iloc[i+1]['Rt'])\n",
    "\n",
    "    # Drawing theta from theta_t+1\n",
    "            g_t = ff_df.iloc[i]['mt'] + B_t*(theta_nt - ff_df.iloc[i+1]['mt'])\n",
    "            H_t = ff_df.iloc[i]['Ct'] - (B_t*ff_df.iloc[i+1]['Rt']*B_t)\n",
    "            theta_nt = np.random.normal(g_t, H_t)\n",
    "        thetas = thetas.append({'Theta': theta_nt}, ignore_index= True)\n",
    "\n",
    "    a = []\n",
    "    for i in range(len(thetas['Theta'])-1,-1,-1):\n",
    "        a.append(thetas['Theta'][i])\n",
    "\n",
    "    a = pd.DataFrame(a)\n",
    "    ff_df['Theta'] = a\n",
    "    ff_df['E_Y_Theta_Sqr'] = (ff_df['Yt']-ff_df['Theta'])*(ff_df['Yt']-ff_df['Theta'])\n",
    "    E_Y_Theta_Sqr_Sum = ff_df['E_Y_Theta_Sqr'].sum()\n",
    "    \n",
    "    ff_df['E_Theta_Theta_Sqr'] = (ff_df['Theta'].diff(periods = 1))*(ff_df['Theta'].diff(periods = 1))\n",
    "    E_Theta_Theta_Sqr_Sum = ff_df['E_Theta_Theta_Sqr'].sum()\n",
    "    ff_df.rename(columns = {'Yt':'Yt'+str(draw),\n",
    "                            'mt':'mt'+str(draw),\n",
    "                            'Ct':'Ct'+str(draw),\n",
    "                            'ft':'ft'+str(draw),\n",
    "                            'Qt':'Qt'+str(draw),\n",
    "                            'At':'At'+str(draw),\n",
    "                            'et':'et'+str(draw),\n",
    "                            'Rt':'Rt'+str(draw),\n",
    "                            'Vt':'Vt'+str(draw),\n",
    "                            'Wt':'Wt'+str(draw),\n",
    "                            'Theta':'Theta'+str(draw),\n",
    "                            'E_Y_Theta_Sqr':'E_Y_Theta_Sqr'+str(draw),\n",
    "                            'E_Theta_Theta_Sqr':'E_Theta_Theta_Sqr'+str(draw)\n",
    "                           }, inplace= True)\n",
    "    df_list.append(ff_df)\n",
    "\n",
    "df_list[-1]\n",
    "# E_Y_Theta_Sqr_Sum\n",
    "\n",
    "#     Yt.plot.line(figsize=(30,10), label='Yt')\n",
    "#     plt.plot(a, label = 'Thetas')\n",
    "#     plt.legend(loc=2, prop={'size': 30})\n",
    "#     plt.show()\n",
    "\n",
    "    # Draw V & W \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "413.83928831538077"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "theta_df = pd.DataFrame()\n",
    "for i in range(len(df_list)):\n",
    "    a = df_list[i].loc[:, df_list[i].columns.str.startswith('Theta')].T\n",
    "    theta_df = theta_df.append(a, ignore_index=True)\n",
    "theta_df\n",
    "theta_df[143].plot()\n",
    "theta_df[143].mean()\n",
    "# theta_df[142].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "theta = 112.00103665658779\n",
      "W (Evolution Variance) = 0.006663606547580611\n"
     ]
    },
    {
     "data": {
      "image/png": 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D7e4uz/InNrYCt/TlvKL71McFwDeWlx/WyI/tEc3afPYZdP4+BDwnybpuyeE53dhEJbkIeDVwaVV9p298PskJ3eWz6c3ffV3Wh5Nc0H2PX9H3tU0y56Cv8zS74NnAZ6rqh8stszafQ5n2O7r0Po3wWXo/GV87xRy/Re+/V58C7uxOlwD/DNzVjd8KrO+7z2u73PeyRu+Y0/s0wSe70z3LcwY8EdgJ7O3OT+3GA7yty3kXsLCGc/pY4KvAz/eNTX0+6f2wOQB8n96e2JXDzB+9Ne993emla5RzH7216eXv0Xd02/5+9/3wSeAO4Hf7HmeBXrl+Dngr3W+mTzjnwK/zpLtgpZzd+LuBPz5s26nN57hO/vkBSWrQtJdlJEkTYLlLUoMsd0lqkOUuSQ2y3CWpQZa7JDXIcpekBv0/N0e+5nXjyioAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 0\n",
    "\n",
    "theta_df = pd.DataFrame()\n",
    "for i in range(len(df_list)):\n",
    "    a = df_list[i].loc[:, df_list[i].columns.str.startswith('Theta')].T\n",
    "    theta_df = theta_df.append(a, ignore_index=True)\n",
    "theta_df\n",
    "theta_df[n].plot()\n",
    "print(f\"theta = {theta_df[n].mean()}\")\n",
    "\n",
    "evolutionVar_df = pd.DataFrame()\n",
    "for i in range(len(df_list)):\n",
    "    a = df_list[i].loc[:, df_list[i].columns.str.startswith('V')].T\n",
    "    evolutionVar_df = evolutionVar_df.append(a, ignore_index=True)\n",
    "evolutionVar_df[n].plot()\n",
    "print(f\"W (Evolution Variance) = {evolutionVar_df[n].mean()}\")\n",
    "\n",
    "# ObservationVar_df = pd.DataFrame()\n",
    "# for i in range(len(df_list)):\n",
    "#     a = df_list[i].loc[:, df_list[i].columns.str.startswith('W')].T\n",
    "#     ObservationVar_df = ObservationVar_df.append(a, ignore_index=True)\n",
    "# ObservationVar_df[n].plot()\n",
    "# print(f\"V (Observational Variance) = {ObservationVar_df[n].mean()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "evolutionVar_df = pd.DataFrame()\n",
    "for i in range(len(df_list)):\n",
    "    a = df_list[i].loc[:, df_list[i].columns.str.startswith('V')].T\n",
    "    evolutionVar_df = evolutionVar_df.append(a, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
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       "      <td>0.004350</td>\n",
       "      <td>0.004350</td>\n",
       "      <td>0.004350</td>\n",
       "      <td>0.004350</td>\n",
       "      <td>0.004350</td>\n",
       "      <td>0.004350</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004350</td>\n",
       "      <td>0.004350</td>\n",
       "      <td>0.004350</td>\n",
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       "      <td>0.004350</td>\n",
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       "      <td>0.004350</td>\n",
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       "      <td>0.004184</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.004184</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>0.004398</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.004417</td>\n",
       "      <td>0.004417</td>\n",
       "      <td>0.004417</td>\n",
       "      <td>0.004417</td>\n",
       "      <td>0.004417</td>\n",
       "      <td>0.004417</td>\n",
       "      <td>0.004417</td>\n",
       "      <td>0.004417</td>\n",
       "      <td>0.004417</td>\n",
       "      <td>0.004417</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004417</td>\n",
       "      <td>0.004417</td>\n",
       "      <td>0.004417</td>\n",
       "      <td>0.004417</td>\n",
       "      <td>0.004417</td>\n",
       "      <td>0.004417</td>\n",
       "      <td>0.004417</td>\n",
       "      <td>0.004417</td>\n",
       "      <td>0.004417</td>\n",
       "      <td>0.004417</td>\n",
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       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>1995</td>\n",
       "      <td>0.003637</td>\n",
       "      <td>0.003637</td>\n",
       "      <td>0.003637</td>\n",
       "      <td>0.003637</td>\n",
       "      <td>0.003637</td>\n",
       "      <td>0.003637</td>\n",
       "      <td>0.003637</td>\n",
       "      <td>0.003637</td>\n",
       "      <td>0.003637</td>\n",
       "      <td>0.003637</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003637</td>\n",
       "      <td>0.003637</td>\n",
       "      <td>0.003637</td>\n",
       "      <td>0.003637</td>\n",
       "      <td>0.003637</td>\n",
       "      <td>0.003637</td>\n",
       "      <td>0.003637</td>\n",
       "      <td>0.003637</td>\n",
       "      <td>0.003637</td>\n",
       "      <td>0.003637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1996</td>\n",
       "      <td>0.003782</td>\n",
       "      <td>0.003782</td>\n",
       "      <td>0.003782</td>\n",
       "      <td>0.003782</td>\n",
       "      <td>0.003782</td>\n",
       "      <td>0.003782</td>\n",
       "      <td>0.003782</td>\n",
       "      <td>0.003782</td>\n",
       "      <td>0.003782</td>\n",
       "      <td>0.003782</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003782</td>\n",
       "      <td>0.003782</td>\n",
       "      <td>0.003782</td>\n",
       "      <td>0.003782</td>\n",
       "      <td>0.003782</td>\n",
       "      <td>0.003782</td>\n",
       "      <td>0.003782</td>\n",
       "      <td>0.003782</td>\n",
       "      <td>0.003782</td>\n",
       "      <td>0.003782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1997</td>\n",
       "      <td>0.004737</td>\n",
       "      <td>0.004737</td>\n",
       "      <td>0.004737</td>\n",
       "      <td>0.004737</td>\n",
       "      <td>0.004737</td>\n",
       "      <td>0.004737</td>\n",
       "      <td>0.004737</td>\n",
       "      <td>0.004737</td>\n",
       "      <td>0.004737</td>\n",
       "      <td>0.004737</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004737</td>\n",
       "      <td>0.004737</td>\n",
       "      <td>0.004737</td>\n",
       "      <td>0.004737</td>\n",
       "      <td>0.004737</td>\n",
       "      <td>0.004737</td>\n",
       "      <td>0.004737</td>\n",
       "      <td>0.004737</td>\n",
       "      <td>0.004737</td>\n",
       "      <td>0.004737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1998</td>\n",
       "      <td>0.003781</td>\n",
       "      <td>0.003781</td>\n",
       "      <td>0.003781</td>\n",
       "      <td>0.003781</td>\n",
       "      <td>0.003781</td>\n",
       "      <td>0.003781</td>\n",
       "      <td>0.003781</td>\n",
       "      <td>0.003781</td>\n",
       "      <td>0.003781</td>\n",
       "      <td>0.003781</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003781</td>\n",
       "      <td>0.003781</td>\n",
       "      <td>0.003781</td>\n",
       "      <td>0.003781</td>\n",
       "      <td>0.003781</td>\n",
       "      <td>0.003781</td>\n",
       "      <td>0.003781</td>\n",
       "      <td>0.003781</td>\n",
       "      <td>0.003781</td>\n",
       "      <td>0.003781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1999</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.004378</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 144 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6    \\\n",
       "0     5.000000  5.000000  5.000000  5.000000  5.000000  5.000000  5.000000   \n",
       "1     0.004350  0.004350  0.004350  0.004350  0.004350  0.004350  0.004350   \n",
       "2     0.004184  0.004184  0.004184  0.004184  0.004184  0.004184  0.004184   \n",
       "3     0.004398  0.004398  0.004398  0.004398  0.004398  0.004398  0.004398   \n",
       "4     0.004417  0.004417  0.004417  0.004417  0.004417  0.004417  0.004417   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "1995  0.003637  0.003637  0.003637  0.003637  0.003637  0.003637  0.003637   \n",
       "1996  0.003782  0.003782  0.003782  0.003782  0.003782  0.003782  0.003782   \n",
       "1997  0.004737  0.004737  0.004737  0.004737  0.004737  0.004737  0.004737   \n",
       "1998  0.003781  0.003781  0.003781  0.003781  0.003781  0.003781  0.003781   \n",
       "1999  0.004378  0.004378  0.004378  0.004378  0.004378  0.004378  0.004378   \n",
       "\n",
       "           7         8         9    ...       134       135       136  \\\n",
       "0     5.000000  5.000000  5.000000  ...  5.000000  5.000000  5.000000   \n",
       "1     0.004350  0.004350  0.004350  ...  0.004350  0.004350  0.004350   \n",
       "2     0.004184  0.004184  0.004184  ...  0.004184  0.004184  0.004184   \n",
       "3     0.004398  0.004398  0.004398  ...  0.004398  0.004398  0.004398   \n",
       "4     0.004417  0.004417  0.004417  ...  0.004417  0.004417  0.004417   \n",
       "...        ...       ...       ...  ...       ...       ...       ...   \n",
       "1995  0.003637  0.003637  0.003637  ...  0.003637  0.003637  0.003637   \n",
       "1996  0.003782  0.003782  0.003782  ...  0.003782  0.003782  0.003782   \n",
       "1997  0.004737  0.004737  0.004737  ...  0.004737  0.004737  0.004737   \n",
       "1998  0.003781  0.003781  0.003781  ...  0.003781  0.003781  0.003781   \n",
       "1999  0.004378  0.004378  0.004378  ...  0.004378  0.004378  0.004378   \n",
       "\n",
       "           137       138       139       140       141       142       143  \n",
       "0     5.000000  5.000000  5.000000  5.000000  5.000000  5.000000  5.000000  \n",
       "1     0.004350  0.004350  0.004350  0.004350  0.004350  0.004350  0.004350  \n",
       "2     0.004184  0.004184  0.004184  0.004184  0.004184  0.004184  0.004184  \n",
       "3     0.004398  0.004398  0.004398  0.004398  0.004398  0.004398  0.004398  \n",
       "4     0.004417  0.004417  0.004417  0.004417  0.004417  0.004417  0.004417  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "1995  0.003637  0.003637  0.003637  0.003637  0.003637  0.003637  0.003637  \n",
       "1996  0.003782  0.003782  0.003782  0.003782  0.003782  0.003782  0.003782  \n",
       "1997  0.004737  0.004737  0.004737  0.004737  0.004737  0.004737  0.004737  \n",
       "1998  0.003781  0.003781  0.003781  0.003781  0.003781  0.003781  0.003781  \n",
       "1999  0.004378  0.004378  0.004378  0.004378  0.004378  0.004378  0.004378  \n",
       "\n",
       "[2000 rows x 144 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evolutionVar_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f88165aa150>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "evolutionVar_df[0].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yt199</th>\n",
       "      <th>mt199</th>\n",
       "      <th>Ct199</th>\n",
       "      <th>ft199</th>\n",
       "      <th>Qt199</th>\n",
       "      <th>At199</th>\n",
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       "      <th>Wt199</th>\n",
       "      <th>Theta199</th>\n",
       "      <th>E_Y_Theta_Sqr199</th>\n",
       "      <th>E_Theta_Theta_Sqr199</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>111.999955</td>\n",
       "      <td>0.004453</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>200.004461</td>\n",
       "      <td>0.999978</td>\n",
       "      <td>2.000000</td>\n",
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       "      <td>7.637798e-09</td>\n",
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       "      <td>1</td>\n",
       "      <td>118.0</td>\n",
       "      <td>115.002472</td>\n",
       "      <td>0.002229</td>\n",
       "      <td>111.999955</td>\n",
       "      <td>0.008914</td>\n",
       "      <td>0.500416</td>\n",
       "      <td>6.000045</td>\n",
       "      <td>0.004461</td>\n",
       "      <td>0.004453</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>115.002423</td>\n",
       "      <td>8.985466e+00</td>\n",
       "      <td>9.015071</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>132.0</td>\n",
       "      <td>120.684171</td>\n",
       "      <td>0.001489</td>\n",
       "      <td>115.002472</td>\n",
       "      <td>0.006689</td>\n",
       "      <td>0.334266</td>\n",
       "      <td>16.997528</td>\n",
       "      <td>0.002236</td>\n",
       "      <td>0.004453</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>120.684117</td>\n",
       "      <td>1.280492e+02</td>\n",
       "      <td>32.281639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>129.0</td>\n",
       "      <td>122.775356</td>\n",
       "      <td>0.001120</td>\n",
       "      <td>120.684171</td>\n",
       "      <td>0.005949</td>\n",
       "      <td>0.251470</td>\n",
       "      <td>8.315829</td>\n",
       "      <td>0.001496</td>\n",
       "      <td>0.004453</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>122.775296</td>\n",
       "      <td>3.874694e+01</td>\n",
       "      <td>4.373032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>121.0</td>\n",
       "      <td>122.416707</td>\n",
       "      <td>0.000900</td>\n",
       "      <td>122.775356</td>\n",
       "      <td>0.005581</td>\n",
       "      <td>0.202015</td>\n",
       "      <td>-1.775356</td>\n",
       "      <td>0.001127</td>\n",
       "      <td>0.004453</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>122.416650</td>\n",
       "      <td>2.006897e+00</td>\n",
       "      <td>0.128627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>139</td>\n",
       "      <td>606.0</td>\n",
       "      <td>408.969796</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>400.710871</td>\n",
       "      <td>0.004640</td>\n",
       "      <td>0.040231</td>\n",
       "      <td>205.289129</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>0.004453</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>408.969989</td>\n",
       "      <td>3.882083e+04</td>\n",
       "      <td>68.209880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>140</td>\n",
       "      <td>508.0</td>\n",
       "      <td>412.953844</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>408.969796</td>\n",
       "      <td>0.004640</td>\n",
       "      <td>0.040231</td>\n",
       "      <td>99.030204</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>0.004453</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>412.954044</td>\n",
       "      <td>9.033734e+03</td>\n",
       "      <td>15.872696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>141</td>\n",
       "      <td>461.0</td>\n",
       "      <td>414.886768</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>412.953844</td>\n",
       "      <td>0.004640</td>\n",
       "      <td>0.040231</td>\n",
       "      <td>48.046156</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>0.004453</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>414.886988</td>\n",
       "      <td>2.126410e+03</td>\n",
       "      <td>3.736271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>142</td>\n",
       "      <td>390.0</td>\n",
       "      <td>413.885561</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>414.886768</td>\n",
       "      <td>0.004640</td>\n",
       "      <td>0.040231</td>\n",
       "      <td>-24.886768</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>0.004453</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>413.885786</td>\n",
       "      <td>5.705308e+02</td>\n",
       "      <td>1.002404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>143</td>\n",
       "      <td>432.0</td>\n",
       "      <td>414.614313</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>413.885561</td>\n",
       "      <td>0.004640</td>\n",
       "      <td>0.040230</td>\n",
       "      <td>18.114439</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>0.004453</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>414.614562</td>\n",
       "      <td>3.022535e+02</td>\n",
       "      <td>0.531114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Yt199       mt199     Ct199       ft199       Qt199     At199  \\\n",
       "0    112.0  111.999955  0.004453  110.000000  200.004461  0.999978   \n",
       "1    118.0  115.002472  0.002229  111.999955    0.008914  0.500416   \n",
       "2    132.0  120.684171  0.001489  115.002472    0.006689  0.334266   \n",
       "3    129.0  122.775356  0.001120  120.684171    0.005949  0.251470   \n",
       "4    121.0  122.416707  0.000900  122.775356    0.005581  0.202015   \n",
       "..     ...         ...       ...         ...         ...       ...   \n",
       "139  606.0  408.969796  0.000179  400.710871    0.004640  0.040231   \n",
       "140  508.0  412.953844  0.000179  408.969796    0.004640  0.040231   \n",
       "141  461.0  414.886768  0.000179  412.953844    0.004640  0.040231   \n",
       "142  390.0  413.885561  0.000179  414.886768    0.004640  0.040231   \n",
       "143  432.0  414.614313  0.000179  413.885561    0.004640  0.040230   \n",
       "\n",
       "          et199       Rt199     Vt199     Wt199    Theta199  E_Y_Theta_Sqr199  \\\n",
       "0      2.000000  200.000008  0.004453  0.000008  111.999913      7.637798e-09   \n",
       "1      6.000045    0.004461  0.004453  0.000008  115.002423      8.985466e+00   \n",
       "2     16.997528    0.002236  0.004453  0.000008  120.684117      1.280492e+02   \n",
       "3      8.315829    0.001496  0.004453  0.000008  122.775296      3.874694e+01   \n",
       "4     -1.775356    0.001127  0.004453  0.000008  122.416650      2.006897e+00   \n",
       "..          ...         ...       ...       ...         ...               ...   \n",
       "139  205.289129    0.000187  0.004453  0.000008  408.969989      3.882083e+04   \n",
       "140   99.030204    0.000187  0.004453  0.000008  412.954044      9.033734e+03   \n",
       "141   48.046156    0.000187  0.004453  0.000008  414.886988      2.126410e+03   \n",
       "142  -24.886768    0.000187  0.004453  0.000008  413.885786      5.705308e+02   \n",
       "143   18.114439    0.000187  0.004453  0.000008  414.614562      3.022535e+02   \n",
       "\n",
       "     E_Theta_Theta_Sqr199  \n",
       "0                     NaN  \n",
       "1                9.015071  \n",
       "2               32.281639  \n",
       "3                4.373032  \n",
       "4                0.128627  \n",
       "..                    ...  \n",
       "139             68.209880  \n",
       "140             15.872696  \n",
       "141              3.736271  \n",
       "142              1.002404  \n",
       "143              0.531114  \n",
       "\n",
       "[144 rows x 13 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Yt = df['Passengers']\n",
    "df_list = list()\n",
    "\n",
    "\n",
    "for draw in range(200):\n",
    "    time_index = 0\n",
    "    m_t = None\n",
    "    c_t = None\n",
    "    m0 = 110\n",
    "    C0 = 200\n",
    "    E_Y_Theta_Sqr_Sum = 0 \n",
    "    E_Theta_Theta_Sqr_Sum = 0\n",
    "    \n",
    "    if draw  == 0:\n",
    "        V = 5\n",
    "        W = 1\n",
    "   \n",
    "    else:\n",
    "        V = invgamma.rvs((1+len(Yt)),scale=(0.6+E_Y_Theta_Sqr_Sum))\n",
    "        W = invgamma.rvs((1+len(Yt)-1)/2,scale=(0.001+E_Theta_Theta_Sqr_Sum)/2)\n",
    "        \n",
    "    ff_df = pd.DataFrame(columns = ['Yt', 'mt', 'Ct', 'ft', 'Qt', 'At', 'et', 'Rt'])\n",
    "    \n",
    "    for Y in Yt:\n",
    "        if time_index == 0:\n",
    "            m_pt = m0\n",
    "            c_pt = C0\n",
    "            time_index += 1\n",
    "        else:\n",
    "            m_pt = m_t \n",
    "            c_pt = c_t\n",
    "            time_index += 1\n",
    "\n",
    "        f_t = m_pt\n",
    "        V_t = V\n",
    "        W_t = W\n",
    "        R_t = c_pt + W\n",
    "        Q_t = R_t + V\n",
    "        A_t = R_t/Q_t\n",
    "        e_t = Y - f_t \n",
    "        c_t = (A_t*V)\n",
    "        m_t = f_t + ((A_t*e_t))\n",
    "        ff_df = ff_df.append({'Yt': Y,\n",
    "                        'mt': m_t,\n",
    "                        'ft':f_t,\n",
    "                        'Ct': c_t,\n",
    "                        'Qt': Q_t,\n",
    "                        'At': A_t,\n",
    "                        'et': e_t,\n",
    "                        'Rt': R_t,\n",
    "                        'Vt': V_t,\n",
    "                        'Wt': W_t\n",
    "\n",
    "                       }, ignore_index=True)\n",
    "\n",
    "\n",
    "    # Yt.plot.line(figsize=(40,10), label='Yt')\n",
    "    # # plt.plot(ff_df['mt'], label = 'mt')\n",
    "    # # plt.legend()\n",
    "    # # plt.show()\n",
    "\n",
    "    # ff_df.plot.line(figsize=(10,10))\n",
    "    # plt.legend(loc=2, prop={'size': 15})\n",
    "\n",
    "    m_T = ff_df.iloc[-1]['mt']\n",
    "    c_T = ff_df.iloc[-1]['Ct']\n",
    "    theta_T = np.random.normal(m_T, c_T)\n",
    "\n",
    "    thetas = pd.DataFrame(columns = ['Theta'])\n",
    "\n",
    "    for i in range(len(ff_df['Yt'])-1,-1,-1):\n",
    "        if i == len(ff_df['Yt'])-1:\n",
    "            theta_nt = theta_T\n",
    "            #print(i, round(theta_nt,1))\n",
    "\n",
    "        else:\n",
    "    #         print()\n",
    "    #         print(i, round(theta_nt, 1))\n",
    "            B_t = (ff_df.iloc[i]['Ct']/ff_df.iloc[i+1]['Rt'])\n",
    "\n",
    "    # Drawing theta from theta_t+1\n",
    "            g_t = ff_df.iloc[i]['mt'] + B_t*(theta_nt - ff_df.iloc[i+1]['mt'])\n",
    "            H_t = ff_df.iloc[i]['Ct'] - (B_t*ff_df.iloc[i+1]['Rt']*B_t)\n",
    "            theta_nt = np.random.normal(g_t, H_t)\n",
    "        thetas = thetas.append({'Theta': theta_nt}, ignore_index= True)\n",
    "\n",
    "    a = []\n",
    "    for i in range(len(thetas['Theta'])-1,-1,-1):\n",
    "        a.append(thetas['Theta'][i])\n",
    "\n",
    "    a = pd.DataFrame(a)\n",
    "    ff_df['Theta'] = a\n",
    "    ff_df['E_Y_Theta_Sqr'] = (ff_df['Yt']-ff_df['Theta'])*(ff_df['Yt']-ff_df['Theta'])\n",
    "    E_Y_Theta_Sqr_Sum = ff_df['E_Y_Theta_Sqr'].sum()\n",
    "    \n",
    "    ff_df['E_Theta_Theta_Sqr'] = (ff_df['Theta'].diff(periods = 1))*(ff_df['Theta'].diff(periods = 1))\n",
    "    E_Theta_Theta_Sqr_Sum = ff_df['E_Theta_Theta_Sqr'].sum()\n",
    "    ff_df.rename(columns = {'Yt':'Yt'+str(draw),\n",
    "                            'mt':'mt'+str(draw),\n",
    "                            'Ct':'Ct'+str(draw),\n",
    "                            'ft':'ft'+str(draw),\n",
    "                            'Qt':'Qt'+str(draw),\n",
    "                            'At':'At'+str(draw),\n",
    "                            'et':'et'+str(draw),\n",
    "                            'Rt':'Rt'+str(draw),\n",
    "                            'Vt':'Vt'+str(draw),\n",
    "                            'Wt':'Wt'+str(draw),\n",
    "                            'Theta':'Theta'+str(draw),\n",
    "                            'E_Y_Theta_Sqr':'E_Y_Theta_Sqr'+str(draw),\n",
    "                            'E_Theta_Theta_Sqr':'E_Theta_Theta_Sqr'+str(draw)\n",
    "                           }, inplace= True)\n",
    "    df_list.append(ff_df)\n",
    "\n",
    "df_list[-1]\n",
    "# E_Y_Theta_Sqr_Sum\n",
    "\n",
    "#     Yt.plot.line(figsize=(30,10), label='Yt')\n",
    "#     plt.plot(a, label = 'Thetas')\n",
    "#     plt.legend(loc=2, prop={'size': 30})\n",
    "#     plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "theta = 305.70672113664585\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 103\n",
    "\n",
    "theta_df = pd.DataFrame()\n",
    "for i in range(len(df_list)):\n",
    "    a = df_list[i].loc[:, df_list[i].columns.str.startswith('Theta')].T\n",
    "    theta_df = theta_df.append(a, ignore_index=True)\n",
    "theta_df\n",
    "theta_df[n].plot()\n",
    "print(f\"theta = {theta_df[n].mean()}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}