Training a DeepAR model with GluonTS

DeepAR is a state-of-the-art forecasting method that utilizes autoregressive recurrent networks to predict future values of time series data. Amazon introduced it; it was designed for forecasting tasks that can benefit from longer horizons, such as demand forecasting. The method is particularly powerful when there’s a need to generate forecasts for multiple related time series.

Getting ready

We’ll use the same dataset as in the previous recipe:

from gluonts.dataset.repository.datasets import get_dataset
dataset = get_dataset("nn5_daily_without_missing", regenerate=False)

Now, let’s see how to build a DeepAR model with this data.

How to do it…

We start by formatting the data for training:

1. Let’s do this by using the ListDataset data structure:

   from gluonts.dataset.common import ListDataset
   from gluonts.dataset.common import FieldName
   train_ds = ListDataset(
       ...