Common Modeling Patterns for Time Series
We reviewed a few major and common building blocks of a deep learning (DL) system, specifically suited for time series, in the last chapter. Now that we know what those blocks are, it’s time for a more practical lesson. Let’s see how we can put these common blocks together in various common ways in which time series forecasting is modeled using the dataset we have been working with all through this book.
In this chapter, we will be covering these main topics:
- Tabular regression
- Single-step-ahead recurrent neural networks
- Sequence-to-sequence models
