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Pattern Detection and Analysis in Financial Time Series Using Suffix Arrays

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Book cover Financial Decision Making Using Computational Intelligence

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 70))

Abstract

The current chapter focuses on data-mining techniques in exploring time series of financial data and more specifically of foreign exchange currency rates’ fluctuations. The data-mining techniques used attempt to analyze time series and extract, if possible, valuable information about pattern periodicity that might be hidden behind huge amount of unformatted and vague information. Such information is of great importance because it might be used to interpret correlations among different events regarding markets or even to forecast future behavior. In the present chapter a new methodology has been introduced to take advantage of suffix arrays in data mining instead of the commonly used data structure suffix trees. Although suffix arrays require high-storage capacity, in the proposed algorithm they can be constructed in linear time O(n) or O(nlogn) using an external database management system which allows better and faster results during analysis process. The proposed methodology is also extended to detect repeated patterns in time series with time complexity of O(nlogn). This along with the capability of external storage creates a critical advantage for an overall efficient data-mining analysis regarding construction of time series data structure and periodicity detection.

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Notes

  1. 1.

    www.bankofcanada.ca/rates/exchange/10-year-converter.

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Author information

Authors and Affiliations

  1. Hellenic American University, Manchester, NH, USA

    Konstantinos F. Xylogiannopoulos & Panagiotis Karampelas

  2. University of Calgary, Calgary, AB, Canada

    Reda Alhajj

Authors
  1. Konstantinos F. Xylogiannopoulos
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  2. Panagiotis Karampelas
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  3. Reda Alhajj
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Corresponding author

Correspondence to Panagiotis Karampelas .

Editor information

Editors and Affiliations

  1. Dept. Production Engineering, and Management, Technical University of Crete, University Campus, Chania, 731 00, Greece

    Michael Doumpos

  2. , Department of Production Engineering and, Technical University of Crete, University Campus, Chania, 73100, Greece

    Constantin Zopounidis

  3. , Department of Industrial and Systems Eng, University of Florida, Weil Hall 401, Gainesville, 32611, Florida, USA

    Panos M. Pardalos

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Xylogiannopoulos, K.F., Karampelas, P., Alhajj, R. (2012). Pattern Detection and Analysis in Financial Time Series Using Suffix Arrays. In: Doumpos, M., Zopounidis, C., Pardalos, P. (eds) Financial Decision Making Using Computational Intelligence. Springer Optimization and Its Applications, vol 70. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-3773-4_5

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