In this section, we will illustrate how to build RNNs using the Keras library for various scenarios. The first set of models includes regression and classification of univariate and multivariate time series. The second set of tasks focuses on text data for sentiment analysis using text data converted to word embeddings (see Chapter 15, Word Embeddings).
More specifically, we'll first demonstrate how to prepare time series data to predict the next value for univariate time series with a single LSTM layer to predict stock index values.
Next, we will add depth and use stacked LSTM layers combined with learned embeddings and one-hot-encoded categorical data to build an RNN model with three distinct inputs that classifies asset price movements. Finally, we will demonstrate how to model multivariate time series using RNNs.
In the second...
