modeltime

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

forecast_plot

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.


modeltime_workflow

A streamlined workflow for forecasting

Learning More

687474703a2f2f696d672e796f75747562652e636f6d2f76692f656c516234567a52494e672f302e6a7067

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.

GitHub