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Deep Learning for Time Series, simplified.

Important: This package is exprimental. Functions may change until the package matures.

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.

GluonTS in R

Using deep_ar(), which connects to GluonTS DeepAREstimator().


# Fit a GluonTS DeepAR Model
model_fit_deepar <- deep_ar(
    id                    = "id",
    freq                  = "M",
    prediction_length     = 24,
    lookback_length       = 36,
    epochs                = 10, 
    num_batches_per_epoch = 50,
    learn_rate            = 0.001,
    num_layers            = 2,
    dropout               = 0.10
) %>%
    set_engine("gluonts_deepar") %>%
    fit(value ~ ., training(m750_splits))

# Forecast with 95% Confidence Interval
) %>%
    modeltime_calibrate(new_data = testing(m750_splits)) %>%
        new_data      = testing(m750_splits),
        actual_data   = m750,
        conf_interval = 0.95
    ) %>%
    plot_modeltime_forecast(.interactive = FALSE)


modeltime.gluonts is currently available on GitHub only. Not on CRAN yet.


Required: Python Environment Setup

Important: Use install_gluonts() to set up the “r-gluonts” python environment used by modeltime.gluonts. You only need to do this once, when you first set up the package.

# GluonTS Installation
# - This sets up the Python Environment
# - Only need to run 1-time, then you're set!

Learning More


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