Temporal data mining using multilevel-local polynomial models

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

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

3 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 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 languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning - IDEAL 2000
Subtitle of host publicationdata mining, financial engineering, and intelligent agents
EditorsKwong Sak Leung, Lai-Wan Chan, Helen Meng
Place of PublicationBerlin
PublisherSpringer, Springer Nature
Number of pages7
ISBN (Print)3540414509
Publication statusPublished - 2000
Event2nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2000 - Shatin, N.T., Hong Kong
Duration: 13 Dec 200015 Dec 2000

Publication series

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


Other2nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2000
Country/TerritoryHong Kong
CityShatin, N.T.


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