GluonTS Deep Learning in R.
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.
Make Your First DeepAR Model
Make your first
deep_ar() model, which connects to the GluonTS
DeepAREstimator(). For a more detailed walkthough, visit our Getting Started 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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