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modeltime

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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.

<a href="https://www.youtube.com/watch?v=-bCelif-ENY" target="_blank"> <p style="text-align:center;"> <img src= "vignettes/modeltime-video.jpg" alt="Introduction to Modeltime" width="60%"/> </p> <p style="text-align:center"> (Click to Watch on YouTube) </p> </a>

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

<img src="vignettes/forecast_plot.jpg" width="100%" style="display: block; margin: auto;" />

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.

<hr> <div class="figure" style="text-align: center"> <img src="vignettes/modeltime_workflow.jpg" alt="A streamlined workflow for forecasting" width="100%" /> <p class="caption"> A streamlined workflow for forecasting </p> </div> <hr>

Meet the modeltime ecosystem

Learn a growing ecosystem of forecasting packages

<div class="figure" style="text-align: center"> <img src="man/figures/modeltime_ecosystem.jpg" alt="The modeltime ecosystem is growing" width="100%" /> <p class="caption"> The modeltime ecosystem is growing </p> </div>

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

<a href="https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting/" target="_blank"><img src="https://www.filepicker.io/api/file/bKyqVAi5Qi64sS05QYLk" alt="High-Performance Time Series Forecasting Course" width="100%" style="box-shadow: 0 0 5px 2px rgba(0, 0, 0, .5);"/></a>

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.

<p class="text-center" style="font-size:24px;"> Become the Time Series Expert for your organization. </p> <br> <p class="text-center" style="font-size:30px;"> <a href="https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting">Take the High-Performance Time Series Forecasting Course</a> </p>