Effective and Efficient Shape-Based Pattern Detection over Streaming Time Series

• Published in: IEEE transactions on knowledge and data engineering Vol. 24; no. 2; pp. 265 - 278
• Main Authors: Yueguo Chen, Ke Chen, Nascimento, M. A.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2012; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Amplitudes; Assembling; Distance measure; Euclidean distance; Feature extraction; Networks; pattern detection; Pattern matching; Real time; shifting and scaling; Surveillance; Temporal logic; Time measurement; Time series; Time series analysis
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2010.223

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.