modeltime

The time series forecasting package for the tidymodels ecosystem.

Features & Benefits

Modeltime unlocks time series models and machine learning in one framework

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) for using best practices to forecast.

Interactive plotting by default

All plots incorporate both plotly (interactive) and ggplot2 (static) visualizations. This means you can quickly add forecasts to shiny apps, rmarkdown documents, and more.

Tutorials

• Getting Started with Modeltime: A walkthrough of the 6-Step Process for using modeltime to forecast

• Modeltime Documentation: Learn how to use modeltime, find Modeltime Models, and extend modeltime so you can use new algorithms inside the Modeltime Workflow.

Installation

Install the development version from with:

# 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. The course is coming soon.

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.

Signup for the Time Series Course waitlist