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Abstract:
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The subsequence matching in a large time-series database has been an interesting problem. Many
methods have been proposed that cope with this problem in an adequate extent. One of the good ideas is
reducing properly the dimensionality of time-series data. In this paper, we propose a new method to reduce
the dimensionality of high-dimensional time-series data. The method is simpler than existing ones based on
the discrete Fourier transform and the discrete cosine transform. Furthermore, our dimensionality reduction
may be executed in parallel. The method is used to time-series data matching problem and it decreases
drastically the complexity of the corresponding algorithm. The method preserves planar geometric blocks
and it is also applied to minimum bounding rectangles as well. Copyright ?? 2011 Inderscience Enterprises
Ltd. |