A Low-Cost Representation for Similarity Search of Time-Series Pattern Based on Minimum Bounding Rectangle

A fundamental problem in whole sequence matching and subsequence matching is the problem of representation of time series. In the last decade many high level representations of time series have been proposed for data mining which involve a trade-off between accuracy and compactness. In this paper the author proposes a novel time series representation called GMBR based on Minimum Bounding Rectangle in which the binary idea is applied into the MBR. The experiments have been performed on synthetic, as well as real data sequences to evaluate the proposed method. The experiment demonstrates that 69-92 percent of irrelevant sequences are pruned using the proposed method.