Temporal Data Mining Using Hidden Markov-Local Polynomial Models

Authors:
Weiqiang Lin;Mehmet A. Orgun;Graham J. Williams
Affiliations:
-;-;-
Venue:
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Year:
2001

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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 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.