Temporal data mining using hidden Markov-local polynomial models

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

doi:10.1007/3-540-45357-1_35

Temporal data mining using hidden Markov-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

9 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 similarity and periodicity patterns. At the first level, a structural-based 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 local polynomial analysis; and then the third level based on hidden Markov-local polynomial models (HMLPMs), finds global patterns from a DTS set.We demonstrate our method on the analysis of "Exchange Rates Patterns" between the U.S. dollar and the United Kingdom Pound.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining
Subtitle of host publication	5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16–18, 2001 Proceedings
Editors	David Cheung, Graham J. Williams, Qing Li
Place of Publication	Berlin; New York
Publisher	Springer, Springer Nature
Pages	324-335
Number of pages	12
ISBN (Electronic)	9783540453574
ISBN (Print)	3540419101, 9783540419105
DOIs	https://doi.org/10.1007/3-540-45357-1_35
Publication status	Published - Apr 2001
Event	5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD - 2001 - Hong Kong, China Duration: 16 Apr 2001 → 18 Apr 2001

Other

Other	5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD - 2001
Country/Territory	China
City	Hong Kong
Period	16/04/01 → 18/04/01

Keywords

Discrete-valued time series
Hidden Markov models
Local polynomial modelling
Periodicity analysis
Similarity patterns
Temporal data mining

Access to Document

10.1007/3-540-45357-1_35

Cite this

Lin, W., Orgun, M. A., & Williams, G. J. (2001). Temporal data mining using hidden Markov-local polynomial models. In D. Cheung, G. J. Williams, & Q. Li (Eds.), Advances in Knowledge Discovery and Data Mining: 5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16–18, 2001 Proceedings (pp. 324-335). Springer, Springer Nature. https://doi.org/10.1007/3-540-45357-1_35

Lin, Weiqiang ; Orgun, Mehmet A. ; Williams, Graham J. / Temporal data mining using hidden Markov-local polynomial models. Advances in Knowledge Discovery and Data Mining: 5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16–18, 2001 Proceedings. editor / David Cheung ; Graham J. Williams ; Qing Li. Berlin; New York : Springer, Springer Nature, 2001. pp. 324-335

@inproceedings{ab6a57fbc3b14075ae790dd1f01ba76b,

title = "Temporal data mining using hidden Markov-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 similarity and periodicity patterns. At the first level, a structural-based 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 local polynomial analysis; and then the third level based on hidden Markov-local polynomial models (HMLPMs), finds global patterns from a DTS set.We demonstrate our method on the analysis of {"}Exchange Rates Patterns{"} between the U.S. dollar and the United Kingdom Pound.",

keywords = "Discrete-valued time series, Hidden Markov models, Local polynomial modelling, Periodicity analysis, Similarity patterns, Temporal data mining",

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

year = "2001",

month = apr,

doi = "10.1007/3-540-45357-1_35",

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booktitle = "Advances in Knowledge Discovery and Data Mining",

publisher = "Springer, Springer Nature",

address = "United States",

note = "5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD - 2001 ; Conference date: 16-04-2001 Through 18-04-2001",

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Lin, W, Orgun, MA & Williams, GJ 2001, Temporal data mining using hidden Markov-local polynomial models. in D Cheung, GJ Williams & Q Li (eds), Advances in Knowledge Discovery and Data Mining: 5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16–18, 2001 Proceedings. Springer, Springer Nature, Berlin; New York, pp. 324-335, 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD - 2001, Hong Kong, China, 16/04/01. https://doi.org/10.1007/3-540-45357-1_35

Temporal data mining using hidden Markov-local polynomial models. / Lin, Weiqiang; Orgun, Mehmet A.; Williams, Graham J.
Advances in Knowledge Discovery and Data Mining: 5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16–18, 2001 Proceedings. ed. / David Cheung; Graham J. Williams; Qing Li. Berlin; New York: Springer, Springer Nature, 2001. p. 324-335.

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 similarity and periodicity patterns. At the first level, a structural-based 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 local polynomial analysis; and then the third level based on hidden Markov-local polynomial models (HMLPMs), finds global patterns from a DTS set.We demonstrate our method on the analysis of "Exchange Rates Patterns" between the U.S. dollar and the United Kingdom Pound.

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 similarity and periodicity patterns. At the first level, a structural-based 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 local polynomial analysis; and then the third level based on hidden Markov-local polynomial models (HMLPMs), finds global patterns from a DTS set.We demonstrate our method on the analysis of "Exchange Rates Patterns" between the U.S. dollar and the United Kingdom Pound.

KW - Discrete-valued time series

KW - Hidden Markov models

KW - Local polynomial modelling

KW - Periodicity analysis

KW - Similarity patterns

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Temporal data mining using hidden Markov-local polynomial models

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