Spiking neural networks for financial data prediction

Abstract

In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, for financial time series prediction is introduced with the aim of exploiting the inherent temporal capabilities of the spiking neural model. The performance of the spiking neural network was benchmarked against two “traditional”, rate-encoded, neural networks; a Multi-Layer Perceptron network and a Functional Link Neural Network. Three nonstationary and noisy time series are used to test these simulations: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return, for both 1-Step and 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown, Signal-To-Noise ratio, and Normalised Mean Square Error. This work demonstrated the applicability of polychronous spiking network to financial data forecasting and that it has the potential to function more effectively than traditional neural networks, in nonstationary environments.