Chapter 5: Time Series Models

There are many datasets that are generated naturally in a sequence that is separated by a quantum of time, such as ocean waves that come to the shore every few minutes or transactions in the stock market that happen every few microseconds. Models that forecast when the next wave will hit the shore or what the price of the next stock transactions could be, by analyzing the history of previous occurrences, are a type of data science algorithm known as Time Series models. While traditional time series methods have long been used for forecasting, using Deep Learning, we can use advanced approaches for better results. In this chapter, we will focus on how to build commonly used Deep Learning-based time series models such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM), using PyTorch Lightning to perform time series forecasting.

In this chapter, we will start with a brief introduction to time series problems and then see a use case...