Temporal Data Mining Using Multilevel-Local Polynominal Models

Authors:
Weiqiang Lin;Mehmet A. Orgun;Graham J. Williams
Affiliations:
-;-;-
Venue:
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Year:
2000

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PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
An application of time-changing feature selection

Data Mining

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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.