The time series forecasting package for the tidymodels ecosystem.
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.
Install the CRAN version:
Or, install the development version:
No need to switch back and forth between various frameworks. modeltime unlocks machine learning & classical time series analysis.
arima_reg(), arima_boost(), & exp_smoothing()).prophet_reg() & prophet_boost())parsnip model: rand_forest(), boost_tree(), linear_reg(), mars(), svm_rbf() to forecastModeltime incorporates a simple workflow (see Getting Started with Modeltime) for using best practices to forecast.
A streamlined workflow for forecasting
My Talk on High-Performance Time Series Forecasting
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. You will learn:
Modeltime - 30+ Models (Prophet, ARIMA, XGBoost, Random Forest, & many more)GluonTS (Competition Winners)