Enhancing DWT for recent-biased dimension reduction of 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:
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Year:
2006

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Abstract

In many applications, old data in time series become less important as time elapses, which is a big challenge to traditional techniques for dimension reduction. To improve Discrete Wavelet Transform (DWT) for effective dimension reduction in this kind of applications, a new method, largest-latest-DWT, is designed by keeping the largest k coefficients out of the latest w coefficients at each level of DWT transform. Its efficiency and effectiveness is demonstrated by our experiments.