Preventing meaningless stock time series pattern discovery by changing perceptually important point detection

Tak Chung Fu, Fu Lai Korris Chung, Wing Pong Robert Luk, Chak Man Ng

Research output: Journal article publicationConference articleAcademic researchpeer-review

12 Citations (Scopus)

Abstract

Discovery of interesting or frequently appearing time series patterns is one of the important tasks in various time series data mining applications. However, recent research criticized that discovering subsequence patterns in time series using clustering approaches is meaningless. It is due to the presence of trivial matched subsequences in the formation of the time series subsequences using sliding window method. The objective of this paper is to propose a threshold-free approach to improve the method for segmenting long stock time series into subsequences using sliding window. The proposed approach filters the trivial matched subsequences by changing Perceptually Important Point (PIP) detection and reduced the dimension by PIP identification.
Original languageEnglish
Pages (from-to)1171-1174
Number of pages4
JournalLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume3613
Issue numberPART I
Publication statusPublished - 27 Oct 2005
EventSecond International Confernce on Fuzzy Systems and Knowledge Discovery, FSKD 2005 - Changsha, China
Duration: 27 Aug 200529 Aug 2005

ASJC Scopus subject areas

  • Hardware and Architecture

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