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.
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)
install.packages("modeltime", dependencies = TRUE)
remotes::install_github("business-science/modeltime", dependencies = TRUE)
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.
A streamlined workflow for forecasting
Modeltime incorporates a streamlined workflow (see Getting Started with Modeltime) for using best practices to forecast.
Learn a growing ecosystem of forecasting packages
Modeltime is part of a growing ecosystem of Modeltime forecasting packages.
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.
Become the forecasting expert for your organization
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).
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
- 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.