Enhancing DWT for recent-biased dimension reduction of time

Publisher:
Springer
Publication Type:
Conference Proceeding
Citation:
AI 2006: Advances in Artificial Intelligence, 2006, pp. 1048 - 1053
Issue Date:
2006-01
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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.
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