Machine Learning for Algorithmic Trading - Second Edition

19

RNNs for Multivariate Time Series and Sentiment Analysis

The previous chapter showed how convolutional neural networks (CNNs) are designed to learn features that represent the spatial structure of grid-like data, especially images, but also time series. This chapter introduces recurrent neural networks (RNNs) that specialize in sequential data where patterns evolve over time and learning typically requires memory of preceding data points.

Feedforward neural networks (FFNNs) treat the feature vectors for each sample as independent and identically distributed. Consequently, they do not take prior data points into account when evaluating the current observation. In other words, they have no memory.

The one- and two-dimensional convolutional ...