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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{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
<img src="vignettes/logo-modeltime.png" width="147" height="170" align="right" />
<!-- badges: start -->
[](https://travis-ci.org/business-science/modeltime)
[](https://codecov.io/gh/business-science/modeltime)
[](https://cran.r-project.org/package=modeltime)
<!-- badges: end -->
The time series forecasting package for the `tidymodels` ecosystem.
## 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
Install the CRAN version:
``` r
install.packages("modeltime")
```
Or, install the development version:
``` r
remotes::install_github("business-science/modeltime")
```
## Features & Benefits
#### 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
#### A streamlined workflow for forecasting
Modeltime incorporates a [simple workflow (see Getting Started with Modeltime)](https://business-science.github.io/modeltime/articles/getting-started-with-modeltime.html) for using best practices to forecast.
<hr>
```{r, echo=F, out.width='100%', fig.align='center', fig.cap="A streamlined workflow for forecasting"}
knitr::include_graphics("vignettes/modeltime_workflow.jpg")
```
<hr>
# Learning More
<a href="https://www.youtube.com/embed/elQb4VzRINg" target="_blank"><img src="http://img.youtube.com/vi/elQb4VzRINg/0.jpg" alt="Anomalize" width="100%" height="450"/></a>
[_My Talk on High-Performance Time Series Forecasting_](https://youtu.be/elQb4VzRINg)
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 MILLIONS of dollars.__ Imagine what will happen to your career if you can provide your organization a "High-Performance Time Series Forecasting System" (HPTSF System).
I teach how to build a HPTFS System in my __High-Performance Time Series Forecasting Course__. If interested in learning Scalable High-Performance Forecasting Strategies then [take my 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)
- NEW - 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.
<p class="text-center" style="font-size:30px;">
<a href="https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting">Unlock the High-Performance Time Series Forecasting Course</a>
</p>