Parallel Dimensionality Reduction Transformation for Time-Series Data

The subsequence matching in large time-series databases has been being an interesting problem. Many methods have been proposed that cope with this problem in an adequate extend. One of good ideas is reducing properly the dimensionality of time-series data. In this paper, we propose a 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. It preserves planar geometric blocks and may be applied to minimum bounding rectangles as well.