Efficient Similarity Search for Time Series Data Based on the Minimum Distance

We address the problem of efficient similarity search based on the minimum distance in large time serV( databases. Most of prO ious wor is focused on similarK y matching andr etr)) al of timeserKO based on the Euclidean distance. However as we demonstr te in this paper , the Euclidean distance has limitations as a similarK y measur ement. It is sensitive to the absolute o#sets of time sequences, so two time sequences that have similar shapes but with different vertical positions may be classified as dissimilar The minimum distance is a mor suitable similarK y measur ment than the Euclidean distance in many applications, wher the shape of time ser es is a major consider ation. To suppor t minimum distance quer ies, most ofpr vious wor has the pr p r cessing step of ver(0(3 shifting that norVB8B8K each time sequence by its mean befor indexing. In this paper , we p r pose a novel and fast indexing scheme, called the segmented mean var iation indexing(SMV-indexing).Our indexing scheme can match time ser es of similar shapes without verO cal shifting and guarV tees no false dismissals. Sever) exper)33 ts ar pe r for(0 onrV l data(stockpr ce movement) to measure the performance of our indexing scheme. Experiment