Pattern Detection and Analysis in Financial Time Series Using Suffix Arrays

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.KeywordsTime SeriesDatabase Management SystemSuffix TreeWeekly DataFinancial Time SeriesThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.