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The motif tracking algorithm

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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 (MTA), a novel immune inspired (IS) 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 utilization 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.

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Authors and Affiliations

  1. School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, UK

    William Wilson, Phil Birkin & Uwe Aickelin

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  1. William Wilson
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  2. Phil Birkin
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  3. Uwe Aickelin
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Correspondence to William Wilson.

Additional information

William Wilson graduated from the University of Wales Aberystwyth, UK, in 1996 where he received a degree in economics and financial accounting. After a number of years working in industry, he returned to Aberystwyth University in 2002 where he received a Master’s degree in computer science.

He is currently a Ph. D. candidate in the Department of Computer Science at Nottingham University.

His research interests include artificial immune systems, immune memory, time series analysis, and motif detection.

Phil Birkin received a mathematics degree and a computer science degree from the Open University, UK, in 1993 and 1995, respectively. He has worked in the computing industry for over 30 years. He is currently a Ph. D. candidate in researching robotics at the University of Nottingham.

His research interests include robot football, artificial immune systems, and fuzzy logic.

Uwe Aickelin received a management science degree from the University of Mannheim, Germany, in 1996 and a European Master and Ph.D. in management science from the University of Wales, Swansea, UK, in 1996 and 1999, respectively.

Following his Ph. D., he joined the University of the West of England in Bristol, where he worked for three years in the Mathematics Department as a lecturer in operational research. In 2002, he accepted a lectureship in computer science at the University of Bradford, mainly focusing on computer security. Since 2003 he has worked for the University of Nottingham in the School of Computer Science where he is now a reader in computer science and director of the Interdisciplinary Optimization Laboratory. He currently holds an EPSRC advanced fellowship focusing on artificial immune systems, anomaly detection and mathematical modelling. He has been awarded EPSRC research funding as principal investigator (including an adventure grant and two IDEAS factory projects) on topics including artificial immune systems, danger theory, computer security, robotics and agent based simulation. He is an associate editor of the IEEE Transactions on Evolutionary Computation, the assistant editor of the Journal of the Operational Research Society, and an editorial board member of Evolutionary Intelligence. He is a member of IEEE.

His research interests include mathematical modelling, heuristic optimization, artificial immune systems, and innate immunology applied to computer security problems.

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Wilson, W., Birkin, P. & Aickelin, U. The motif tracking algorithm. Int. J. Autom. Comput. 5, 32–44 (2008). https://doi.org/10.1007/s11633-008-0032-0

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  • DOI: https://doi.org/10.1007/s11633-008-0032-0

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