Recurrent neural networks (RNNs) are a group of neural networks that are built to model sequential data. In the last few chapters, we looked at using convolutional layers to learn features from images. Recurrent layers are equally as useful when we want to learn features from a sequence of values that are all related: xt, xt-1, xt-2, xt-3.
In this chapter, we will talk about how to use RNNs for time series problems, which are unsurprisingly problems involving a sequence of data points placed in temporal or chronological order.
We will cover the following topics in this chapter:
- Introducing recurrent neural networks
- Time series problems
- Using an LSTM for time series prediction
