The Application of the Neural Network Model Inspired by the Immune System in Financial Time Series Prediction

This paper presents a novel application of the self-organised multilayer perceptrons inspired by the immune algorithm in financial time series prediction. The simulation results were compared with the multilayer perceptrons and the functional link neural networks. The prediction capability of the various neural networks was tested on ten different data sets; the US/UK exchange rates, the JP/US exchange rate, the US/EU exchange rates, NASDAQO time series, NASDAQC time series, DJIAO time series, DJIAC time series, DJUAO time series, DJUAC time series and the oil price. A new training algorithm was utilized with the self-organised multilayer perceptrons neural network that is inspired by the immune using weight decay, the simulation results indicated significant improvement of the proposed training over the standard network.