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About the developer

business-science
167 Stars 27 Forks Other 279 Commits 12 Opened issues

Description

Modeltime unlocks time series forecast models and machine learning in one framework

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modeltime

Travis build
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The time series forecasting package for the

tidymodels
ecosystem.

Tutorials

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
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.


A streamlined workflow for forecasting

A streamlined workflow for forecasting


Learning More

Anomalize

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

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