The Performance of Immune-Based Neural Network with Financial Time Series Prediction

Abstract

This paper presents the use of immune-based neural networks that include multilayer perceptron (MLP) and functional neural
network for the prediction of financial time series signals. Extensive simulations for the prediction of one-and five-steps-ahead
of stationary and non-stationary time series were performed which indicate that immune-based neural networks in most cases
demonstrated advantages in capturing chaotic movement in the financial signals with an improvement in the profit return and
rapid convergence over MLPs.