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

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: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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 language	English
Title of host publication	Fuzzy Systems and Knowledge Discovery - Second International Conference, FSKD 2005, Proceedings
Publisher	Springer Verlag
Pages	1171-1174
Number of pages	4
ISBN (Print)	9783540283126
Publication status	Published - 1 Jan 2006
Event	2nd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2005 - Changsa, China Duration: 27 Aug 2005 → 29 Aug 2005

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3613 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2005
Country/Territory	China
City	Changsa
Period	27/08/05 → 29/08/05

ASJC Scopus subject areas

General Computer Science
Theoretical Computer Science

Cite this

Fu, T. C., Chung, F. L. K., Luk, W. P. R., & Ng, C. M. (2006). Preventing meaningless stock time series pattern discovery by changing perceptually important point detection. In Fuzzy Systems and Knowledge Discovery - Second International Conference, FSKD 2005, Proceedings (pp. 1171-1174). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3613 LNAI). Springer Verlag.

Fu, Tak Chung ; Chung, Fu Lai Korris ; Luk, Wing Pong Robert et al. / Preventing meaningless stock time series pattern discovery by changing perceptually important point detection. Fuzzy Systems and Knowledge Discovery - Second International Conference, FSKD 2005, Proceedings. Springer Verlag, 2006. pp. 1171-1174 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Preventing meaningless stock time series pattern discovery by changing perceptually important point detection",

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.",

author = "Fu, {Tak Chung} and Chung, {Fu Lai Korris} and Luk, {Wing Pong Robert} and Ng, {Chak Man}",

year = "2006",

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language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Fuzzy Systems and Knowledge Discovery - Second International Conference, FSKD 2005, Proceedings",

address = "Germany",

note = "2nd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2005 ; Conference date: 27-08-2005 Through 29-08-2005",

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Fu, TC, Chung, FLK, Luk, WPR & Ng, CM 2006, Preventing meaningless stock time series pattern discovery by changing perceptually important point detection. in Fuzzy Systems and Knowledge Discovery - Second International Conference, FSKD 2005, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3613 LNAI, Springer Verlag, pp. 1171-1174, 2nd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2005, Changsa, China, 27/08/05.

Preventing meaningless stock time series pattern discovery by changing perceptually important point detection. / Fu, Tak Chung; Chung, Fu Lai Korris; Luk, Wing Pong Robert et al.
Fuzzy Systems and Knowledge Discovery - Second International Conference, FSKD 2005, Proceedings. Springer Verlag, 2006. p. 1171-1174 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3613 LNAI).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AB - 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.

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Fu TC, Chung FLK, Luk WPR, Ng CM. Preventing meaningless stock time series pattern discovery by changing perceptually important point detection. In Fuzzy Systems and Knowledge Discovery - Second International Conference, FSKD 2005, Proceedings. Springer Verlag. 2006. p. 1171-1174. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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

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