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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Zhao, Y., Zhang, C., Zhang, S. (2005). A Recent-Biased Dimension Reduction Technique for Time Series Data. In: Ho, T.B., Cheung, D., Liu, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2005. Lecture Notes in Computer Science(), vol 3518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11430919_87
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DOI: https://doi.org/10.1007/11430919_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26076-9
Online ISBN: 978-3-540-31935-1
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