Temporal data mining using hidden Markov-local polynomial models

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

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

9 Citations (Scopus)


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 languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16–18, 2001 Proceedings
EditorsDavid Cheung, Graham J. Williams, Qing Li
Place of PublicationBerlin; New York
PublisherSpringer, Springer Nature
Number of pages12
ISBN (Electronic)9783540453574
ISBN (Print)3540419101, 9783540419105
Publication statusPublished - Apr 2001
Event5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD - 2001 - Hong Kong, China
Duration: 16 Apr 200118 Apr 2001


Other5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD - 2001
CityHong Kong


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


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