Modeltime unlocks time series forecast models and machine learning in one framework
The time series forecasting package for the
tidymodelsecosystem.
Getting Started with Modeltime: A walkthrough of the 6-Step Process for using
modeltimeto forecast
Modeltime Documentation: Learn how to use
modeltime, find Modeltime Models, and extend
modeltimeso you can use new algorithms inside the Modeltime Workflow.
Install the CRAN version:
install.packages("modeltime")
Or, install the development version:
remotes::install_github("business-science/modeltime")
No need to switch back and forth between various frameworks.
modeltimeunlocks machine learning & classical time series analysis.
arima_reg(),
arima_boost(), &
exp_smoothing()).
prophet_reg()&
prophet_boost())
parsnipmodel:
rand_forest(),
boost_tree(),
linear_reg(),
mars(),
svm_rbf()to forecast
Modeltime 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)