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.

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