Hypercomplex neural network in time series forecasting of stock data

TL;DR

This paper evaluates hypercomplex neural networks for stock time series forecasting, showing they can achieve comparable accuracy with fewer parameters and faster training, with input order affecting performance.

Contribution

It introduces and tests hypercomplex neural network architectures for time series prediction, demonstrating efficiency and effectiveness advantages over traditional models.

Findings

Hypercomplex dense layers achieve similar accuracy with fewer parameters.

Hypercomplex networks train faster than other architectures.

Input order significantly impacts model performance.

Abstract

The goal of this paper is to test three classes of neural network (NN) architectures based on four-dimensional (4D) hypercomplex algebras for time series prediction. We evaluate different architectures, varying the input layers to include convolutional, Long Short-Term Memory (LSTM), or dense hypercomplex layers for 4D algebras. Four related Stock Market time series are used as input data, with the prediction focused on one of them. Hyperparameter optimization for each architecture class was conducted to compare the best-performing neural networks within each class. The results indicate that, in most cases, architectures with hypercomplex dense layers achieve similar Mean Absolute Error (MAE) accuracy compared to other architectures, but with significantly fewer trainable parameters. Consequently, hypercomplex neural networks demonstrate the ability to learn and process time series data faster than the other tested architectures. Additionally, it was found that the ordering of the input time series have a notable impact on effectiveness.