Capsule Network Regression Using Information Measures: An Application in Bitcoin Market

Predicting financial markets has always been one of the most challengingissues, attracting the attention of many investors and researchers. In this regard, deeplearning methods have been used a lot recently. Due to the desired results, such networks are always in development and progress. One of the networks that is beingimplemented in various fields is capsule network. The first time the classification capsule network was introduced, it was able to attract a lot of attention with its successon MNIST data 1. In such networks, as in the other ones, the parameters are obtainedby minimizing a loss function. In this paper, we first change the classification capsulenetwork to a regression capsule network by modifying the last layer of the network.Then we use different information measures such as Kullnack-Leibler, Lin-Wang andTriangular information measures as a loss function, and compare their results with wellknown models including Artificial Neural Network (ANN), Convolutional Network(CNN) and Long Short-Term Memory (LSTM) as well as common used loss functionssuch as Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE). Usingappropriate accuracy metrics, it is shown that the capsule network using triangularinformation measure is well able to predict the price of bitcoin for the medium andlong term period including 10, 90 and 180 days.