The Tidymodels Extension for Time Series Modeling.
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)
Tutorials
Getting Started with Modeltime: A walkthrough of the 6-Step Process for using
modeltime
to forecastModeltime Documentation: Learn how to use
modeltime
, findModeltime Models, and extendmodeltime
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.
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.