https://github.com/business-science/modeltime

Modeltime unlocks time series forecast models and machine learning in one framework

https://github.com/business-science/modeltime

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Modeltime unlocks time series forecast models and machine learning in one framework

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%",
  message = F,
  warning = F,
  dpi = 200
)
```

# modeltime



[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/modeltime)](https://cran.r-project.org/package=modeltime)
![](http://cranlogs.r-pkg.org/badges/modeltime?color=brightgreen)
![](http://cranlogs.r-pkg.org/badges/grand-total/modeltime?color=brightgreen)
[![Codecov test coverage](https://codecov.io/gh/business-science/modeltime/branch/master/graph/badge.svg)]( https://app.codecov.io/gh/business-science/modeltime?branch=master)
[![R-CMD-check](https://github.com/business-science/modeltime/workflows/R-CMD-check/badge.svg)](https://github.com/business-science/modeltime/actions)


> Tidy time series forecasting in `R`. 

Mission: Our number 1 goal is to make high-performance time series analysis easier, faster, and more scalable. Modeltime solves this with a simple to use infrastructure for modeling and forecasting time series. 

## Quickstart Video

For those that prefer video tutorials, we have an [11-minute YouTube Video](https://www.youtube.com/watch?v=-bCelif-ENY) that walks you through the Modeltime Workflow. 





(Click to Watch on YouTube)




## Tutorials

- [__Getting Started with Modeltime__](https://business-science.github.io/modeltime/articles/getting-started-with-modeltime.html): A walkthrough of the 6-Step Process for using `modeltime` to forecast

- [__Modeltime Documentation__](https://business-science.github.io/modeltime/): Learn how to __use__ `modeltime`, __find__ _Modeltime Models_, and __extend__ `modeltime` so you can use new algorithms inside the _Modeltime Workflow_. 



## Installation

CRAN version:

``` r
install.packages("modeltime", dependencies = TRUE)
```

Development version:

``` r
remotes::install_github("business-science/modeltime", dependencies = TRUE)
```

## Why modeltime?


> Modeltime unlocks time series models and machine learning in one framework 

```{r, echo=F, out.width='100%', fig.align='center'}
knitr::include_graphics("vignettes/forecast_plot.jpg")
```

No need to switch back and forth between various frameworks. `modeltime` unlocks machine learning & classical time series analysis.

  - __forecast__: Use ARIMA, ETS, and more models coming (`arima_reg()`, `arima_boost()`, & `exp_smoothing()`). 
  - __prophet__: Use Facebook's Prophet algorithm (`prophet_reg()` & `prophet_boost()`)
  - __tidymodels__: Use any `parsnip` model: `rand_forest()`, `boost_tree()`, `linear_reg()`, `mars()`, `svm_rbf()` to forecast 

## Forecast faster

> A streamlined workflow for forecasting

Modeltime incorporates a [streamlined workflow (see Getting Started with Modeltime)](https://business-science.github.io/modeltime/articles/getting-started-with-modeltime.html) for using best practices to forecast.



```{r, echo=F, out.width='100%', fig.align='center', fig.cap="A streamlined workflow for forecasting"}
knitr::include_graphics("vignettes/modeltime_workflow.jpg")
```





## Meet the modeltime ecosystem 

> Learn a growing ecosystem of forecasting packages

```{r, echo=F, out.width='100%', fig.align='center', fig.cap="The modeltime ecosystem is growing"}
knitr::include_graphics("man/figures/modeltime_ecosystem.jpg")
```

Modeltime is part of a __growing ecosystem__ of Modeltime forecasting packages. 

- [Modeltime (Machine Learning)](https://business-science.github.io/modeltime/)

- [Modeltime H2O (AutoML)](https://business-science.github.io/modeltime.h2o/)

- [Modeltime GluonTS (Deep Learning)](https://business-science.github.io/modeltime.gluonts/)

- [Modeltime Ensemble (Blending Forecasts)](https://business-science.github.io/modeltime.ensemble/)

- [Modeltime Resample (Backtesting)](https://business-science.github.io/modeltime.resample/)

- [Timetk (Feature Engineering, Data Wrangling, Time Series Visualization)](https://business-science.github.io/timetk/)


## Summary

Modeltime is an amazing ecosystem for time series forecasting. But it can take a long time to learn: 

- Many algorithms
- Ensembling and Resampling
- Machine Learning
- Deep Learning
- Scalable Modeling: 10,000+ time series

Your probably thinking how am I ever going to learn time series forecasting. Here's the solution that will save you years of struggling. 

## Take the High-Performance Forecasting Course

> Become the forecasting expert for your organization



[_High-Performance Time Series Course_](https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting/)

### Time Series is Changing

Time series is changing. __Businesses now need 10,000+ time series forecasts every day.__ This is what I call a _High-Performance Time Series Forecasting System (HPTSF)_ - Accurate, Robust, and Scalable Forecasting. 

 __High-Performance Forecasting Systems will save companies by improving accuracy and scalability.__ Imagine what will happen to your career if you can provide your organization a "High-Performance Time Series Forecasting System" (HPTSF System).

### How to Learn High-Performance Time Series Forecasting

I teach how to build a HPTFS System in my [__High-Performance Time Series Forecasting Course__](https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting). You will learn:

- __Time Series Machine Learning__ (cutting-edge) with `Modeltime` - 30+ Models (Prophet, ARIMA, XGBoost, Random Forest, & many more)
- __Deep Learning__ with `GluonTS` (Competition Winners)
- __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)
- __Scalable Forecasting__ - Forecast 1000+ time series in parallel
- and more.


Become the Time Series Expert for your organization.




Take the High-Performance Time Series Forecasting Course

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