Forecasting with PyTorch Lightning

In this chapter, we’ll build forecasting models using PyTorch Lightning. We’ll touch on several aspects of this framework, such as creating a data module to handle data preprocessing or creating a LightningModel structure that encapsulates the training process of neural networks. We’ll also explore TensorBoard to monitor the training process of neural networks. Then, we’ll describe a few metrics for evaluating deep neural networks for forecasting, such as Mean Absolute Scaled Error (MASE) and Symmetric Mean Absolute Percentage Error (SMAPE). In this chapter, we’ll focus on multivariate time series, which contain more than one variable.

This chapter will guide you through the following recipes:

• Preparing a multivariate time series for supervised learning
• Training a linear regression model for forecasting with a multivariate time series
• Feedforward neural networks for multivariate time series forecasting...