modeltime

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The time series forecasting package for the

tidymodels

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 CRAN version:

install.packages("modeltime")

Or, install the development version:

remotes::install_github("business-science/modeltime")

Features & Benefits

Modeltime unlocks time series models and machine learning in one framework

No need to switch back and forth between various frameworks.

modeltime

• 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.

A streamlined workflow for forecasting

Learning More

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.

Unlock the High-Performance Time Series Forecasting Course