Dimension reduction for clustering time series using global characteristics

Authors:
Xiaozhe Wang;Kate A. Smith;Rob J. Hyndman
Affiliations:
Faculty of Information Technology, Monash University, Clayton, Victoria, Australia;Faculty of Information Technology, Monash University, Clayton, Victoria, Australia;Department of Econometrics and Business Statistics, Monash University, Clayton, Victoria, Australia
Venue:
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
Year:
2005

Citing 4
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Abstract

Existing methods for time series clustering rely on the actual data values can become impractical since the methods do not easily handle dataset with high dimensionality, missing value, or different lengths. In this paper, a dimension reduction method is proposed that replaces the raw data with some global measures of time series characteristics. These measures are then clustered using a self-organizing map. The proposed approach has been tested using benchmark time series previously reported for time series clustering, and is shown to yield useful and robust clustering.