https://github.com/business-science/modeltime.ensemble
Time Series Ensemble Forecasting
https://github.com/business-science/modeltime.ensemble
ensemble ensemble-learning forecast forecasting modeltime r-package stacking stacking-ensemble tidymodels time time-series timeseries
Last synced: about 1 year ago
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Time Series Ensemble Forecasting
- Host: GitHub
- URL: https://github.com/business-science/modeltime.ensemble
- Owner: business-science
- License: other
- Created: 2020-09-21T01:21:00.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-07-18T20:02:29.000Z (almost 2 years ago)
- Last Synced: 2025-03-31T11:04:06.186Z (about 1 year ago)
- Topics: ensemble, ensemble-learning, forecast, forecasting, modeltime, r-package, stacking, stacking-ensemble, tidymodels, time, time-series, timeseries
- Language: R
- Homepage: https://business-science.github.io/modeltime.ensemble/
- Size: 21.2 MB
- Stars: 78
- Watchers: 4
- Forks: 18
- Open Issues: 11
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- License: LICENSE
Awesome Lists containing this project
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
message = F,
warning = F,
paged.print = FALSE,
fig.path = "man/figures/README-",
# out.width = "100%"
fig.align = 'center'
)
```
# modeltime.ensemble 
[](https://cran.r-project.org/package=modeltime.ensemble)
[](https://github.com/business-science/modeltime.ensemble/actions/workflows/R-CMD-check.yaml)
[](https://app.codecov.io/gh/business-science/modeltime.ensemble?branch=master)
> Ensemble Algorithms for Time Series Forecasting with Modeltime
A `modeltime` extension that implements ___ensemble forecasting methods___ including model averaging, weighted averaging, and stacking.
```{r, echo=F, out.width='100%', fig.align='center'}
knitr::include_graphics("vignettes/stacking.jpg")
```
## Installation
Install the CRAN version:
``` r
install.packages("modeltime.ensemble")
```
Or, install the development version:
``` r
remotes::install_github("business-science/modeltime.ensemble")
```
## Getting Started
1. [Getting Started with Modeltime](https://business-science.github.io/modeltime/articles/getting-started-with-modeltime.html): Learn the basics of forecasting with Modeltime.
2. [Getting Started with Modeltime Ensemble](https://business-science.github.io/modeltime.ensemble/articles/getting-started-with-modeltime-ensemble.html): Learn the basics of forecasting with Modeltime ensemble models.
## Make Your First Ensemble in Minutes
Load the following libraries.
```{r}
library(tidymodels)
library(modeltime)
library(modeltime.ensemble)
library(dplyr)
library(timetk)
```
#### Step 1 - Create a Modeltime Table
Create a _Modeltime Table_ using the `modeltime` package.
```{r}
m750_models
```
#### Step 2 - Make a Modeltime Ensemble
Then turn that Modeltime Table into a ___Modeltime Ensemble.___
```{r}
ensemble_fit <- m750_models %>%
ensemble_average(type = "mean")
ensemble_fit
```
#### Step 3 - Forecast!
To forecast, just follow the [Modeltime Workflow](https://business-science.github.io/modeltime/articles/getting-started-with-modeltime.html).
```{r}
# Calibration
calibration_tbl <- modeltime_table(
ensemble_fit
) %>%
modeltime_calibrate(testing(m750_splits), quiet = FALSE)
# Forecast vs Test Set
calibration_tbl %>%
modeltime_forecast(
new_data = testing(m750_splits),
actual_data = m750
) %>%
plot_modeltime_forecast(.interactive = FALSE)
```
## 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.