Structural pattern discovery in time series database

Weiqiang Lin, Mehmet A. Orgun, Graham J. Williams

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

This study proposes a temporal data mining method to discover qualitative and quantitative patterns in time series databases. The method performs discrete-valued time series (DIS) analysis on time series databases to search for any similarity and periodicity of patterns that are used for knowledge discovery. In our method there are three levels for mining patterns. At the first level, a structural search based on distance measure models is employed to find pattern structures; the second level performs a value-based search on the discovered patterns using a local polynomial analysis; the third level, based on hidden Markov models (HMMs), finds global patterns from a DTS set. As a result, similar and periodic patterns are successfully extracted. We demonstrate our method on the analysis of "Exchange Rate Patterns" between the U.S. dollar and Australian dollar.
Original languageEnglish
Title of host publicationComputational intelligence in economics and finance
EditorsShu-Heng Chen, Paul P. Wang, Tzu-Wen Kuo
Place of PublicationNew York
PublisherSpringer, Springer Nature
Pages262-287
Number of pages26
ISBN (Print)3540440984
Publication statusPublished - 2004

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