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The Motif Tracking Algorithm

Wilson, William; Birkin, Phil; Aickelin, Uwe

Authors

William Wilson

Phil Birkin

Uwe Aickelin



Abstract

The search for patterns or motifs in data represents a problem area of key interest to finance and economic researchers. In this paper we introduce the Motif Tracking Algorithm, a novel immune inspired pattern identification tool that is able to identify unknown motifs of a non specified length which repeat within time series data. The power of the algorithm comes from the fact that it uses a small number of parameters with minimal assumptions regarding the data being examined or the underlying motifs. Our interest lies in applying the algorithm to financial time series data to identify unknown patterns that exist. The algorithm is tested using three separate data sets. Particular suitability to financial data is shown by applying it to oil price data. In all cases the algorithm identifies the presence of a motif population in a fast and efficient manner due to the utilisation of an intuitive symbolic representation. The resulting population of motifs is shown to have considerable potential value for other applications such as forecasting and algorithm seeding.

Citation

Wilson, W., Birkin, P., & Aickelin, U. (2008). The Motif Tracking Algorithm. International Journal of Automation and Computing, 5(January), 32–44

Journal Article Type Article
Acceptance Date Sep 24, 2007
Online Publication Date Jan 1, 2008
Publication Date Jan 1, 2008
Deposit Date Jan 11, 2008
Journal International Journal of Automation and Computing
Print ISSN 1476-8186
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 5
Issue January
Pages 32–44
Keywords motif tracking, pattern identification
Public URL http://eprints.nottingham.ac.uk/id/eprint/720
Publisher URL https://link.springer.com/article/10.1007/s11633-008-0032-0
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf
Additional Information The original publication is available at www.springerlink.com

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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