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 extendmodeltime
so 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. 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
Modeltime incorporates a simple workflow (see Getting Started with Modeltime) for using best practices to forecast.
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:
- 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.