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
- Getting Started with Modeltime: Learn the basics of forecasting with Modeltime.
- Getting Started with Modeltime Ensemble: Learn the basics of forecasting with Modeltime ensemble models.
Make Your First Ensemble in Minutes
Load the following libraries.
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
Modeltime is part of a growing ecosystem of Modeltime forecasting packages.
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-
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