README.Rmd

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output: github_document
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```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%",
  message = F,
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# modeltime

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A scalable time series forecasting toolset that __combines classical algorithms and machine learning into 1 framework.__

```{r, echo=F, out.width='70%', fig.align='center'}
knitr::include_graphics("vignettes/forecast_plot.jpg")
```


## Features & Benefits

Modeltime has a few key features and benefits:

1. __Interactive Plotting by default__ - Modeling algorithms have strengths and weaknesses that come to light when visualized interactively. 
2. __Use Classical Algorithms and Machine Learning Algorithms together__ - No need to switch back and forth between various frameworks. `modeltime` unlocks machine learning & classical time series analysis. 


## Tutorials

- Getting Started - A walkthrough of the 6-Step Process for using `modeltime` to forecast




## Installation

Install the development version from with:

``` r
# install.packages("devtools")
devtools::install_github("business-science/modeltime")
```

# Learning More

I teach `modeltime` in my __Time Series Analysis & Forecasting Course__. If interested in learning Pro-Forecasting Strategies then [join my waitlist](https://mailchi.mp/business-science/time-series-forecasting-course-coming-soon). The course is coming soon. 

```{r, echo=FALSE}
knitr::include_graphics("vignettes/time_series_course.jpg")
```

You will learn:

- Time Series Preprocessing, Noise Reduction, & Anomaly Detection
- Feature engineering using lagged variables & external regressors
- Hyperparameter Tuning
- Time series cross-validation
- Ensembling Multiple Machine Learning & Univariate Modeling Techniques (Competition Winner)
- NEW - Deep Learning with RNNs (Competition Winner)
- and more.

<p class="text-center" style="font-size:30px;">
<a href="https://mailchi.mp/business-science/time-series-forecasting-course-coming-soon">Signup for the Time Series Course waitlist</a>
</p>