modeltime.h2o
Forecasting with H2O AutoML
Modeltime H2O provides an H2O backend to the Modeltime Forecasting Ecosystem. The main algorithm is H2O AutoML, an automatic machine learning library that is built for speed and scale.
# Install Development Version
devtools::install_github("business-science/modeltime.h2o")What’s possible
With the Modeltime Ecosystem, it’s easy to forecast at scale. This forecast was created with H2O AutoML. Try it out in Getting Started with Modeltime H2O.
Meet the modeltime ecosystem
Learn a growing ecosystem of forecasting packages
Modeltime is part of a growing ecosystem of Modeltime forecasting packages.
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.