GluonTS Deep Learning in R.
Modeltime GluonTS integrates the Python GluonTS Deep Learning Library, making it easy to develop forecasts using Deep Learning for those that are comfortable with the Modeltime Forecasting Workflow.
Important: This package is being maintained on GitHub (not CRAN). Please install the GitHub version, which is updated with the latest features:
# Install GitHub Version ::install_github("business-science/modeltime.gluonts") remotes # Install Python Dependencies ::install_gluonts()modeltime.gluonts
For more detailed installation instructions and troubleshooting guidance, visit our Installation Guide.
library(modeltime.gluonts) library(tidymodels) library(tidyverse) # Fit a GluonTS DeepAR Model model_fit_deepar <- deep_ar( id = "id", freq = "M", prediction_length = 24, lookback_length = 48, epochs = 5 ) %>% set_engine("gluonts_deepar") %>% fit(value ~ ., training(m750_splits)) # Forecast with 95% Confidence Interval modeltime_table( model_fit_deepar ) %>% modeltime_calibrate(new_data = testing(m750_splits)) %>% modeltime_forecast( new_data = testing(m750_splits), actual_data = m750, conf_interval = 0.95 ) %>% plot_modeltime_forecast(.interactive = FALSE)
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