Temporal data mining using multilevel-local polynomial models

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

doi:10.1007/3-540-44491-2_27

Temporal data mining using multilevel-local polynomial models

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

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

3 Citations (Scopus)

Abstract

This study proposes a data mining framework to discover qualitative and quantitative patterns in discrete-valued time series(DTS). In our method, there are three levels for mining temporal patterns. At the first level, a structural method based on distance measures through polynomial modelling is employed to find pattern structures; the second level performs a value-based search using local polynomial analysis; and then the third level based on multilevel-local polynomial models(MLPMs), finds global patterns from a DTS set. We demonstrate our method on the analysis of \Exchange Rates Patterns" between the U.S. dollar and Australian dollar.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning - IDEAL 2000
Subtitle of host publication	data mining, financial engineering, and intelligent agents
Editors	Kwong Sak Leung, Lai-Wan Chan, Helen Meng
Place of Publication	Berlin
Publisher	Springer, Springer Nature
Pages	180-186
Number of pages	7
Volume	1983
ISBN (Print)	3540414509
DOIs	https://doi.org/10.1007/3-540-44491-2_27
Publication status	Published - 2000
Event	2nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2000 - Shatin, N.T., Hong Kong Duration: 13 Dec 2000 → 15 Dec 2000

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	1983
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Other

Other	2nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2000
Country/Territory	Hong Kong
City	Shatin, N.T.
Period	13/12/00 → 15/12/00

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10.1007/3-540-44491-2_27

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Lin, W., Orgun, M. A., & Williams, G. J. (2000). Temporal data mining using multilevel-local polynomial models. In K. S. Leung, L.-W. Chan, & H. Meng (Eds.), Intelligent Data Engineering and Automated Learning - IDEAL 2000 : data mining, financial engineering, and intelligent agents (Vol. 1983, pp. 180-186). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1983). Springer, Springer Nature. https://doi.org/10.1007/3-540-44491-2_27

Lin, Weiqiang ; Orgun, Mehmet A. ; Williams, Graham J. / Temporal data mining using multilevel-local polynomial models. Intelligent Data Engineering and Automated Learning - IDEAL 2000 : data mining, financial engineering, and intelligent agents. editor / Kwong Sak Leung ; Lai-Wan Chan ; Helen Meng. Vol. 1983 Berlin : Springer, Springer Nature, 2000. pp. 180-186 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Temporal data mining using multilevel-local polynomial models",

abstract = "This study proposes a data mining framework to discover qualitative and quantitative patterns in discrete-valued time series(DTS). In our method, there are three levels for mining temporal patterns. At the first level, a structural method based on distance measures through polynomial modelling is employed to find pattern structures; the second level performs a value-based search using local polynomial analysis; and then the third level based on multilevel-local polynomial models(MLPMs), finds global patterns from a DTS set. We demonstrate our method on the analysis of \Exchange Rates Patterns{"} between the U.S. dollar and Australian dollar.",

author = "Weiqiang Lin and Orgun, {Mehmet A.} and Williams, {Graham J.}",

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booktitle = "Intelligent Data Engineering and Automated Learning - IDEAL 2000",

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Lin, W, Orgun, MA & Williams, GJ 2000, Temporal data mining using multilevel-local polynomial models. in KS Leung, L-W Chan & H Meng (eds), Intelligent Data Engineering and Automated Learning - IDEAL 2000 : data mining, financial engineering, and intelligent agents. vol. 1983, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1983, Springer, Springer Nature, Berlin, pp. 180-186, 2nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2000, Shatin, N.T., Hong Kong, 13/12/00. https://doi.org/10.1007/3-540-44491-2_27

Temporal data mining using multilevel-local polynomial models. / Lin, Weiqiang; Orgun, Mehmet A.; Williams, Graham J.
Intelligent Data Engineering and Automated Learning - IDEAL 2000 : data mining, financial engineering, and intelligent agents. ed. / Kwong Sak Leung; Lai-Wan Chan; Helen Meng. Vol. 1983 Berlin: Springer, Springer Nature, 2000. p. 180-186 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1983).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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N2 - This study proposes a data mining framework to discover qualitative and quantitative patterns in discrete-valued time series(DTS). In our method, there are three levels for mining temporal patterns. At the first level, a structural method based on distance measures through polynomial modelling is employed to find pattern structures; the second level performs a value-based search using local polynomial analysis; and then the third level based on multilevel-local polynomial models(MLPMs), finds global patterns from a DTS set. We demonstrate our method on the analysis of \Exchange Rates Patterns" between the U.S. dollar and Australian dollar.

AB - This study proposes a data mining framework to discover qualitative and quantitative patterns in discrete-valued time series(DTS). In our method, there are three levels for mining temporal patterns. At the first level, a structural method based on distance measures through polynomial modelling is employed to find pattern structures; the second level performs a value-based search using local polynomial analysis; and then the third level based on multilevel-local polynomial models(MLPMs), finds global patterns from a DTS set. We demonstrate our method on the analysis of \Exchange Rates Patterns" between the U.S. dollar and Australian dollar.

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DO - 10.1007/3-540-44491-2_27

M3 - Conference proceeding contribution

AN - SCOPUS:84944058327

SN - 3540414509

VL - 1983

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Lin W, Orgun MA, Williams GJ. Temporal data mining using multilevel-local polynomial models. In Leung KS, Chan LW, Meng H, editors, Intelligent Data Engineering and Automated Learning - IDEAL 2000 : data mining, financial engineering, and intelligent agents. Vol. 1983. Berlin: Springer, Springer Nature. 2000. p. 180-186. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-44491-2_27

Temporal data mining using multilevel-local polynomial models

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