/ Machine Learning

Modeltime unlocks time series models and machine learning in one framework

Modeltime unlocks time series models and machine learning in one framework

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

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