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Reid, D, Hussain, A and Tawfik, H (2014) Financial time series prediction using spiking neural networks. PLoS ONE, 9 (8). pp. 1-13. ISSN 1932-6203
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PLOS ONE manuscript_23.11.13_withacceptedchanges[1].pdf - Accepted Version Available under License Creative Commons Attribution. Download (195kB) | Preview |
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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. © 2014 Reid et al.
| Item Type: | Article |
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| Uncontrolled Keywords: | MD Multidisciplinary |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Computer Science & Mathematics |
| Publisher: | Public Library of Science |
| Date Deposited: | 23 Nov 2015 08:06 |
| Last Modified: | 04 Sep 2021 13:47 |
| DOI or ID number: | 10.1371/journal.pone.0103656 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/2370 |
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