Fast Subsequence Matching in Time-Series Data bases

We present an efficient indexing method to locate subsequences within a collection of sequences, such that the subsequences match a given (query) pattern within a specified tolerance. The idea is to map each data sequence into a small set of boxes in feature space. Then, these rectangles can be readily indexed using traditional spatial access methods, like the R*-tree [9]. More detailed, we use a sliding window over the data sequence and extract its features; the results is a trail in feature space. We propose an efficient and effective algorithm to divide such trail in sub-trails, which are subsequently represented by their Minimum Bounding Rectangles (MBRs). We also examine queries of varying lengths, and we show how to handle each case efficiently. We implemented our method and carried out experiments on synthetic and real data (stock price movements). We compared the method to sequential scanning, which is the only obvious competitor. The results were excellent: our method accelerated the search time from 3 times up to 100 times