Training an Informer model with NeuralForecast

In this recipe, we’ll explore the neuralforecast Python library to train an Informer model, another Transformer-based deep learning approach for forecasting.

Getting ready

Informer is a Transformer method tailored for long-term forecasting – that is, predicting with a large forecasting horizon. The main difference relative to a vanilla Transformer is that Informer provides an improved self-attention mechanism, which significantly reduces the computational requirements to run the model and generate long-sequence predictions.

In this recipe, we’ll show you how to train Informer using neuralforecast. We’ll use the same dataset as in the previous recipes:

from gluonts.dataset.repository.datasets import get_dataset
dataset = get_dataset('nn5_daily_without_missing')

How to do it…

This time, instead of creating DataModule to handle the data preprocessing, we’ll use the typical...