Warping the Time on Data Streams

Continuously monitoring through time the correlation/distance of multiple data streams is of interest in a variety of applications, including financial analysis, video surveillance, and mining of biological data. However, distance measures commonly adopted for comparing time series, such as Euclidean and Dynamic Time Warping (DTW ), either are known to be inaccurate or are too time-consuming to be applied in a streaming environment. In this paper we propose a novel DTW -like distance measure, called SDTW , which, unlike DTW , can be efficiently updated at each time step and experimentally show that it improves over DTW by orders of magnitude without sacrificing accuracy. For instance, with a sliding window of 512 samples, SDTW is 400 times faster than DTW .