Warping the time on data streams

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摘要

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 Stream-DTW (SDTW), which unlike DTW can be efficiently updated at each time step. We formally and experimentally demonstrate that SDTW speeds up the monitoring process by a factor that grows linearly with the size of the window sliding over the streams. For instance, with a sliding window of 512 samples, SDTW is about 600 times faster than DTW. We also show that SDTW is a tight approximation of DTW, errors never exceeding 10%, and that it consistently outperforms approximations developed for the case of static time series.

论文关键词:Data streams,Dynamic time warping,Sliding window

论文评审过程:Received 12 August 2006, Accepted 12 August 2006, Available online 17 October 2006.

论文官网地址:https://doi.org/10.1016/j.datak.2006.08.012