RNNs for time series with TensorFlow 2
In this section, we illustrate how to build recurrent neural nets using the TensorFlow 2 library for various scenarios. The first set of models includes the 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 16, Word Embeddings for Earnings Calls and SEC Filings).
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'll build a deep RNN with three distinct inputs to classify asset price movements. To this end, we'll combine a two-layer, stacked LSTM with learned embeddings and one-hot encoded categorical data. Finally, we will demonstrate how to model multivariate time series using an RNN.
