کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
379253 659281 2007 21 صفحه PDF سفارش دهید دانلود رایگان
عنوان انگلیسی مقاله ISI
Warping the time on data streams
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موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
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Warping the time on data streams
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Data & Knowledge Engineering - Volume 62, Issue 3, September 2007, Pages 438–458
نویسندگان
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