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.
Installation Requirements
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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-
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