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https://business-science.github.io/modeltime.h2o/
Forecasting with H2O AutoML. Use the H2O Automatic Machine Learning algorithm as a backend for Modeltime Time Series Forecasting.
https://business-science.github.io/modeltime.h2o/
deep-learning forecast forecasting h2o machine-learning modeltime r r-package tidymodels time-series time-series-analysis timeseries
Last synced: 3 months ago
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Forecasting with H2O AutoML. Use the H2O Automatic Machine Learning algorithm as a backend for Modeltime Time Series Forecasting.
- Host: GitHub
- URL: https://business-science.github.io/modeltime.h2o/
- Owner: business-science
- License: other
- Created: 2021-03-04T20:44:26.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2024-01-04T20:44:38.000Z (6 months ago)
- Last Synced: 2024-01-29T17:54:51.712Z (5 months ago)
- Topics: deep-learning, forecast, forecasting, h2o, machine-learning, modeltime, r, r-package, tidymodels, time-series, time-series-analysis, timeseries
- Language: R
- Homepage: https://business-science.github.io/modeltime.h2o/
- Size: 21.1 MB
- Stars: 38
- Watchers: 7
- Forks: 11
- Open Issues: 12
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Lists
- awesome-h2o - modeltime.h2o R package
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# modeltime.h2o 
[](https://cran.r-project.org/package=modeltime.h2o)
[](https://codecov.io/gh/business-science/modeltime.h2o?branch=master)
[](https://github.com/business-science/modeltime.h2o/actions)
> 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.
``` r
# 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](https://business-science.github.io/modeltime.h2o/articles/getting-started.html).
```{r, echo=FALSE}
knitr::include_graphics("man/figures/h2o_forecast_plot.png")
```
## Meet the modeltime ecosystem
> Learn a growing ecosystem of forecasting packages
```{r, echo=F, out.width='100%', fig.align='center', fig.cap="The modeltime ecosystem is growing"}
knitr::include_graphics("man/figures/modeltime_ecosystem.jpg")
```
Modeltime is part of a __growing ecosystem__ of Modeltime forecasting packages.
- [Modeltime (Machine Learning)](https://business-science.github.io/modeltime/)
- [Modeltime H2O (AutoML)](https://business-science.github.io/modeltime.h2o/)
- [Modeltime GluonTS (Deep Learning)](https://business-science.github.io/modeltime.gluonts/)
- [Modeltime Ensemble (Blending Forecasts)](https://business-science.github.io/modeltime.ensemble/)
- [Modeltime Resample (Backtesting)](https://business-science.github.io/modeltime.resample/)
- [Timetk (Feature Engineering, Data Wrangling, Time Series Visualization)](https://business-science.github.io/timetk/)
## Take the High-Performance Forecasting Course
> Become the forecasting expert for your organization
[_High-Performance Time Series Course_](https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting/)
### 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__](https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting). 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.