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modeltime.ensemble

Ensemble Algorithms for Time Series Forecasting with Modeltime

A modeltime extension that implements ensemble forecasting methods including model averaging, weighted averaging, and stacking.

Installation

Install the CRAN version:

install.packages("modeltime.ensemble")

Or, install the development version:

remotes::install_github("business-science/modeltime.ensemble")

Getting Started

1. Getting Started with Modeltime: Learn the basics of forecasting with Modeltime.
2. Getting Started with Modeltime Ensemble: Learn the basics of forecasting with Modeltime ensemble models.

Make Your First Ensemble in Minutes

Load the following libraries.

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.

m750_models
#> # Modeltime Table
#> # A tibble: 3 × 3
#>   .model_id .model     .model_desc            
#>       <int> <list>     <chr>                  
#> 1         1 <workflow> ARIMA(0,1,1)(0,1,1)[12]
#> 2         2 <workflow> PROPHET                
#> 3         3 <workflow> GLMNET

Step 2 - Make a Modeltime Ensemble

Then turn that Modeltime Table into a Modeltime Ensemble.

ensemble_fit <- m750_models %>%
    ensemble_average(type = "mean")

ensemble_fit
#> ── Modeltime Ensemble ───────────────────────────────────────────
#> Ensemble of 3 Models (MEAN)
#> 
#> # Modeltime Table
#> # A tibble: 3 × 3
#>   .model_id .model     .model_desc            
#>       <int> <list>     <chr>                  
#> 1         1 <workflow> ARIMA(0,1,1)(0,1,1)[12]
#> 2         2 <workflow> PROPHET                
#> 3         3 <workflow> GLMNET

Step 3 - Forecast!

To forecast, just follow the Modeltime Workflow.

# 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

The modeltime ecosystem is growing

Modeltime is part of a growing ecosystem of Modeltime forecasting packages.

• Modeltime (Machine Learning)

• Modeltime H2O (AutoML)

• Modeltime GluonTS (Deep Learning)

• Modeltime Ensemble (Blending Forecasts)

• Modeltime Resample (Backtesting)

• Timetk (Feature Engineering, Data Wrangling, Time Series Visualization)

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.

Become the Time Series Expert for your organization.

Take the High-Performance Time Series Forecasting Course

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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