Ensemble Algorithms for Time Series Forecasting with Modeltime
modeltime extension that implements ensemble forecasting
methods including model averaging, weighted averaging, and stacking.
Install the CRAN version:
Or, install the development version:
Load the following libraries.
library(tidymodels) library(modeltime) library(modeltime.ensemble) library(tidyverse) library(timetk)
Create a Modeltime Table using the
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) #> 2 2 <workflow> PROPHET #> 3 3 <workflow> GLMNET
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) #> 2 2 <workflow> PROPHET #> 3 3 <workflow> GLMNET
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)
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