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

Document Type : Research Paper


1 Independent Researcher, Mashhad, Iran

2 macquarie university

3 Université de Caen-LMNO, Caen, France



Predicting financial markets has always been one of the most challenging
issues, attracting the attention of many investors and researchers. In this regard, deep
learning 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 being
implemented 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 success
on MNIST data 1
. In such networks, as in the other ones, the parameters are obtained
by minimizing a loss function. In this paper, we first change the classification capsule
network 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 and
Triangular 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 functions
such as Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE). Using
appropriate accuracy metrics, it is shown that the capsule network using triangular
information measure is well able to predict the price of bitcoin for the medium and
long term period including 10, 90 and 180 days.