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