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Nonlinearity Linkage Detection for Financial Time Series Analysis

Chiotis, T; Clack, CD; (2007) Nonlinearity Linkage Detection for Financial Time Series Analysis. In: Lipson, H, (ed.) Proceedings of the 9th annual conference on Genetic and evolutionary computation. (pp. pp. 1179-1186). ACM: Association for Computing Machinery: London, England, United Kingdom. Green open access

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Abstract

Standard detection algorithms for nonlinearity linkage fail when applied to typical problems in the analysis of financial time-series data. We explain how this failure arises when standard algorithms are applied naïvely, how linkage detection needs to be applied directly to the observed data samples, and how this raises problems that are not addressedby current techniques. We extend the existing DSMDGA linkage detection algorithm and present a new system that can determine the required nonlinearity linkage in observed data samples for financial time series. The new system has been evaluated on synthetic datasets and experimental results are provided. The sensitivity of the system to changes in both the problem and the algorithm parameters has also been explored and we discuss the results. We present evidence of the success of the new system and identify areas for further work.

Type: Proceedings paper
Title: Nonlinearity Linkage Detection for Financial Time Series Analysis
Event: Annual Conference of Genetic and Evolutionary Computation Conference
Location: London, UK
Dates: 07 July 2007 - 11 July 2007
ISBN-13: 978-1-59593-697-4
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/1276958
Publisher version: http://dx.doi.org/10.1145/1276958
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Genetic Algorithms, Linkage Learning, Epistasis, Composition, Perturbation, Hierarchical, Time Series, Finance
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10087100
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