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modeltime

Tidy time series forecasting in R.

Mission: Our number 1 goal is to make high-performance time series analysis easier, faster, and more scalable. Modeltime solves this with a simple to use infrastructure for modeling and forecasting time series.

Quickstart Video

For those that prefer video tutorials, we have an 11-minute YouTube Video that walks you through the Modeltime Workflow.

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

CRAN version:

install.packages("modeltime", dependencies = TRUE)

Development version:

remotes::install_github("business-science/modeltime", dependencies = TRUE)

Why modeltime?

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

Forecast faster

A streamlined workflow for forecasting

Modeltime incorporates a streamlined workflow (see Getting Started with Modeltime) for using best practices to forecast.

A streamlined workflow for forecasting

Meet the modeltime ecosystem

Learn a growing ecosystem of forecasting packages

The modeltime ecosystem is growing

Modeltime is part of a growing ecosystem of Modeltime forecasting packages.

• Modeltime (Machine Learning)

• Modeltime H2O (AutoML)

• Modeltime GluonTS (Deep Learning)

• Modeltime Ensemble (Blending Forecasts)

• Modeltime Resample (Backtesting)

• Timetk (Feature Engineering, Data Wrangling, Time Series Visualization)

Summary

Modeltime is an amazing ecosystem for time series forecasting. But it can take a long time to learn:

• Many algorithms
• Ensembling and Resampling
• Machine Learning
• Deep Learning
• Scalable Modeling: 10,000+ time series

Your probably thinking how am I ever going to learn time series forecasting. Here’s the solution that will save you years of struggling.

Take the High-Performance Forecasting Course

Become the forecasting expert for your organization

High-Performance Time Series Course

Time Series is Changing

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 by improving accuracy and scalability. Imagine what will happen to your career if you can provide your organization a “High-Performance Time Series Forecasting System” (HPTSF System).

How to Learn High-Performance Time Series Forecasting

I teach how to build a HPTFS System in my High-Performance Time Series Forecasting Course. You will learn:

• Time Series Machine Learning (cutting-edge) with Modeltime - 30+ Models (Prophet, ARIMA, XGBoost, Random Forest, & many more)
• 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.

Become the Time Series Expert for your organization.

Take the High-Performance Time Series Forecasting Course

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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