Finding time series discord based on bit representation clustering

Authors:
Guiling Li;Olli Bräysy;Liangxiao Jiang;Zongda Wu;Yuanzhen Wang
Affiliations:
School of Computer Science, China University of Geosciences, Wuhan 430074, China;VU University Amsterdam, De Boelelaan 1081, 1081 HV Amsterdam, Netherlands and Procomp Solutions Oy, Kiviharjuntie 11, FI-90220 Oulu, Finland;School of Computer Science, China University of Geosciences, Wuhan 430074, China;Oujiang College, Wenzhou University, Wenzhou 325035, Zhejiang, China;School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
Venue:
Knowledge-Based Systems
Year:
2013

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Abstract

The problem of finding time series discord has attracted much attention recently due to its numerous applications and several algorithms have been suggested. However, most of them suffer from high computation cost and cannot satisfy the requirement of real applications. In this paper, we propose a novel discord discovery algorithm BitClusterDiscord which is based on bit representation clustering. Firstly, we use PAA (Piecewise Aggregate Approximation) bit serialization to segment time series, so as to capture the main variation characteristic of time series and avoid the influence of noise. Secondly, we present an improved K-Medoids clustering algorithm to merge several patterns with similar variation behaviors into a common cluster. Finally, based on bit representation clustering, we design two pruning strategies and propose an effective algorithm for time series discord discovery. Extensive experiments have demonstrated that the proposed approach can not only effectively find discord of time series, but also greatly improve the computational efficiency.