title: Financial time series prediction using spiking neural networks.
abstract: In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: 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 and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments.
authors.affiliation: Department of Mathematics and Computer Science, Liverpool Hope University, Liverpool, United Kingdom; School of Computing and Mathematical Sciences Liverpool John Moores University Liverpool, United Kingdom.
authors.forename: David
authors.initials: D
authors.lastname: Reid
authors.affiliation: Department of Mathematics and Computer Science, Liverpool Hope University, Liverpool, United Kingdom; School of Computing and Mathematical Sciences Liverpool John Moores University Liverpool, United Kingdom.
authors.forename: Abir Jaafar
authors.initials: AJ
authors.lastname: Hussain
authors.affiliation: Department of Mathematics and Computer Science, Liverpool Hope University, Liverpool, United Kingdom; School of Computing and Mathematical Sciences Liverpool John Moores University Liverpool, United Kingdom.
authors.forename: Hissam
authors.initials: H
authors.lastname: Tawfik
mesh_terms: D003132:Commerce / Q000639:trends*; D003198:Computer Simulation; D016014:Linear Models; D018803:Models, Economic; D016571:Neural Networks, Computer