Issue No. 02 - February (2012 vol. 24)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.223
Yueguo Chen , Renmin University of China, Beijing
Ke Chen , Zhejiang University, Hangzhou
Mario A. Nascimento , University of Alberta, Edmonton
Existing distance measures of time series such as the euclidean distance, DTW, and EDR are inadequate in handling certain degrees of amplitude shifting and scaling variances of data items. We propose a novel distance measure of time series, Spatial Assembling Distance (SpADe), that is able to handle noisy, shifting, and scaling in both temporal and amplitude dimensions. We further apply the SpADe to the application of streaming pattern detection, which is very useful in trend-related analysis, sensor networks, and video surveillance. Our experimental results on real time series data sets show that SpADe is an effective distance measure of time series. Moreover, high accuracy and efficiency are achieved by SpADe for continuous pattern detection in streaming time series.
Distance measure, time series, shifting and scaling, pattern detection.
Y. Chen, M. A. Nascimento and K. Chen, "Effective and Efficient Shape-Based Pattern Detection over Streaming Time Series," in IEEE Transactions on Knowledge & Data Engineering, vol. 24, no. , pp. 265-278, 2010.