A parallel dimensionality reduction for time-series data and some of its applications

Abstract

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.