https://github.com/business-science/modeltime.ensemble

Time Series Ensemble Forecasting

https://github.com/business-science/modeltime.ensemble

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Time Series Ensemble Forecasting

README.Rmd

---
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 


[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/modeltime.ensemble)](https://cran.r-project.org/package=modeltime.ensemble)
![](http://cranlogs.r-pkg.org/badges/modeltime.ensemble?color=brightgreen)
![](http://cranlogs.r-pkg.org/badges/grand-total/modeltime.ensemble?color=brightgreen)
[![R-CMD-check](https://github.com/business-science/modeltime.ensemble/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/business-science/modeltime.ensemble/actions/workflows/R-CMD-check.yaml)
[![Codecov test coverage](https://codecov.io/gh/business-science/modeltime.ensemble/branch/master/graph/badge.svg)](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.




Take the High-Performance Time Series Forecasting Course

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