Probabilistic forecasting with an LSTM

This recipe will walk you through building an LSTM neural network for probabilistic forecasting using PyTorch Lightning.

Getting ready

In this recipe, we’ll introduce probabilistic forecasting with LSTM networks. This approach combines the strengths of LSTM models in capturing long-term dependencies within sequential data with the nuanced perspective of probabilistic forecasting. This method goes beyond traditional point estimates by predicting a range of possible future outcomes, each accompanied by a probability. This means that we are incorporating uncertainty into forecasts.

This recipe uses the same dataset that we used in Chapter 4, in the Feedforward neural networks for multivariate time series forecasting recipe. We’ll also use the same data module we created in that recipe, which is called MultivariateSeriesDataModule.

Let’s explore how to use this data module to build an LSTM model for probabilistic forecasting...