Efficient subsequence matching in time series databases under time and amplitude transformations

Subsequence matching in large time series databases has attracted a lot of interest and many methods have been proposed that cope with this problem in an adequate extend. However, locating subsequence matches of arbitrary length, under time and amplitude transformations, has received far less attention and is still an open problem. We present an efficient algorithm for variable-length subsequence matching under transformations that guarantees no false dismissals. Further, this algorithm uses a novel similarity criterion for determining similarity under amplitude transformations in a most efficient way. Finally, our algorithm has been tested in various experiments on real data, resulting in a running time improvement of one order of magnitude compared to the naive approach.