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modeltime

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

Model DevelopmentClassical MLR
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Created 2020-04-17 · Updated 2026-09-05 · #16570 today
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mdancho84AlbertoAlmuinhaolivroy

business-science/modeltime is ranked 16,570 with 584 stars.

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Key parameters and specifications
Key parameters and specifications
ParameterValue
LanguageR
CategoryModel Development
SubcategoryClassical ML
KeywordsTimeseries
Stars584
Forks86
Contributors11
Created date2020-04-17
Updated date2026-09-05
Stars in 1 day0
Stars in 7 days0
Rank16,570
Previous rank16,356
Rank change-214
7-day stars rank15,602
Previous 7-day stars rank15,613
7-day stars rank change11
Total stars rank17,149
Previous total stars rank17,118
Total stars rank change-31
CountryUSA
README summary

modeltime coverage]( Tidy time series forecasting in . 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. (Click to Watch on YouTube) Tutorials **Getting Started with Modeltime**: A walkthrough of the 6-Step Process for

using to forecast **Modeltime Documentation**: Learn how to **use** , **find** *Modeltime Models*, and **extend** so you can use new algorithms inside the *Modeltime Workflow*. Installation CRAN version: Development version: Why modeltime? Modeltime unlocks time series models and machine learning in one framework No need to switch back and forth between various frameworks. unlocks machine learning & classical time series analysis. **forecast**: Use ARIMA, ETS, and more models coming ( , , & ). **prophet**: Use Facebook’s Prophet algorithm ( & ) **tidymodels**: Use any model: , , , , 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 - 30+ Models (Prophet, ARIMA, XGBoost, Random Forest, & many more) **Deep Learning** with (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

Frequently asked questions

How many stars and forks are recorded?

business-science/modeltime has 584 stars and 86 forks.

What is the repository rank?

business-science/modeltime is ranked 16,570.

What language and category are recorded?

business-science/modeltime has 584 stars, uses R and belongs to Model Development.

Which repository is closest in rank?

business-science/modeltime is compared with abides-sim/abides, ranked 16,567 with 584 stars.

README

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.

[

(Click to Watch on YouTube)

](https://www.youtube.com/watch?v=-bCelif-ENY)

Tutorials

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.

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

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