Temporal Data Mining Using Hidden Periodicity Analysis

Authors:
Weiqiang Lin;Mehmet A. Orgun
Affiliations:
-;-
Venue:
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Year:
2000

Citing 2
Cited 2

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VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases

Temporal Data Mining Using Hidden Markov-Local Polynomial Models

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

Data mining, often called knowledge discovery in databases (KDD), aims at semiautomatic tools for the analysis of large data sets. This report is first intended to serve as a timely overview of a rapidly emerging area of research, called temporal data mining (that is, data mining from temporal databases and/or discrete time series). We in particular provide a general overview of temporal data mining, motivating the importance of problems in this area, which include formulations of the basic categories of temporal data mining methods, models, techniques and some other related areas. This report also outlines a general framework for analysing discrete time series databases, based on hidden periodicity analysis, and presents the preliminary results of our experiments on the exchange rate data between US dollar and Canadian dollar.