A recent-biased dimension reduction technique for time series data

Authors:
Yanchang Zhao;Chengqi Zhang;Shichao Zhang
Affiliations:
Faculty of Information Technology, University of Technology, Sydney, Australia;Faculty of Information Technology, University of Technology, Sydney, Australia;Faculty of Information Technology, University of Technology, Sydney, Australia
Venue:
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2005

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Abstract

There are many techniques developed for tackling time series and most of them consider every part of a sequence equally. In many applications, however, recent data can often be much more interesting and significant than old data. This paper defines new recent-biased measures for distance and energy, and proposes a recent-biased technique based on DWT for time series in which more recent data are considered more significant. With such a recent-biased technique, the dimension of time series can be reduced while effectively preserving the recent-biased energy. Our experiments have demonstrated the effectiveness of the proposed approach for handling time series.