CNNs for Financial Time Series and Satellite Images
In this chapter, we introduce the first of several specialized deep learning architectures that we will cover in Part 4. Deep convolutional neural networks (CNNs) have enabled superhuman performance in various computer vision tasks such as classifying images and video and detecting and recognizing objects in images. CNNs can also extract signals from time-series data that shares certain characteristics with image data and have been successfully applied to speech recognition (Abdel-Hamid et al. 2014). Moreover, they have been shown to deliver state-of-the-art performance on time-series classification across various domains (Ismail Fawaz et al. 2019).
CNNs are named after a linear algebra operation called a convolution that replaces the general matrix multiplication typical of feedforward networks (discussed in the last chapter) in at least one of their layers. We will show how convolutions work and why they are particularly...
