Pattern Detection and Analysis in Financial Time Series Using Suffix Arrays

Konstantinos F. Xylogiannopoulos (), Panagiotis Karampelas () and Reda Alhajj ()
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Konstantinos F. Xylogiannopoulos: Hellenic American University
Panagiotis Karampelas: Hellenic American University
Reda Alhajj: University of Calgary

Chapter Chapter 5 in Financial Decision Making Using Computational Intelligence, 2012, pp 129-157 from Springer

Abstract: 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.

Keywords: Time Series; Database Management System; Suffix Tree; Weekly Data; Financial Time Series (search for similar items in EconPapers)
Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-1-4614-3773-4_5

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DOI: 10.1007/978-1-4614-3773-4_5

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