Time Series Forecasting using an LSTM version of RNN with PyTorch Forecasting and Torch Lightning

This summary explains how to use Ray to speed up Deep Learning forecasting models for time series prediction by utilizing data and model parallelism. The process involves installing the necessary libraries, initializing Ray and its plugin for PyTorch Lightning, reading in sample data, converting it to PyTorch tensors and defining data loaders, training a PyTorch Forecasting model with the Ray Lightning plugin, and running the code on a laptop or any cloud using Anyscale. By leveraging parallel computing capabilities of Ray, developers can significantly reduce training times for these models, making them more efficient and scalable.