›› 2013,Vol. 28 ›› Issue (2): 300-310.doi: 10.1007/s11390-013-1330-8

所属专题: Artificial Intelligence and Pattern Recognition Data Management and Data Mining

• Special Section on Selected Paper from NPC 2011 • 上一篇    下一篇


Wei Luo1, Marcus Gallagher2, Member, IEEE, and Janet Wiles2, Member, IEEE   

  • 收稿日期:2012-02-15 修回日期:2012-11-14 出版日期:2013-03-05 发布日期:2013-03-05

Parameter-Free Search of Time-Series Discord

Wei Luo1, Marcus Gallagher2, Member, IEEE, and Janet Wiles2, Member, IEEE   

  1. 1 School of Information Technology, Deakin University, Geelong, VIC 3220, Australia;
    2 School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD 4072, Australia
  • Received:2012-02-15 Revised:2012-11-14 Online:2013-03-05 Published:2013-03-05
  • Supported by:

    Support by Australian Research Council Linkage Grant No. LP 0776417.

时间序列异常常常在数据挖掘中被用来描述异常子序列。相比一些其他的搜索算法, 基于复原图的直接搜索算法拥快速和无参数等优点。但是直接搜索算法假设时间序列存在准周期性, 因此限制了该算法的适用性。本文提出一个参考函数和基于它的新的取样策略, 从而去除了直接搜索算法中的准周期假设。实验结果显示新的算法具有更高的效率和鲁棒性。

Abstract: Time-series discord is widely used in data mining applications to characterize anomalous subsequences in time series. Compared to some other discord search algorithms, the direct search algorithm based on the recurrence plot shows the advantage of being fast and parameter free. The direct search algorithm, however, relies on quasi-periodicity in input time series, an assumption that limits the algorithm's applicability. In this paper, we eliminate the periodicity assumption from the direct search algorithm by proposing a reference function for subsequences and a new sampling strategy based on the reference function. These measures result in a new algorithm with improved efficiency and robustness, as evidenced by our empirical evaluation.

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