On-line motif detection in time series with SwiftMotif
On-line motif detection in time series with SwiftMotif
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DOI：
10.1016/j.patcog.2009.05.004
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发表时间：
2009-11
期刊：
影响因子：
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通讯作者：
E. Fuchs;Thiemo Gruber;Jiri Nitschke;B. Sick
中科院分区：
文献类型：
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作者：
E. Fuchs;Thiemo Gruber;Jiri Nitschke;B. Sick
本文介绍了SwiftMotif，一种新的技术，用于在时间序列中的在线模体检测。例如，使用这种技术，可以发现频繁发生的时间模式或异常。基序检测是基于融合的方法从两个世界：概率建模和相似性测量技术相结合，非常快速的多项式最小二乘近似技术。一个时间序列分割的数据流分割方法，段建模的正态分布与时间相关的手段和恒定的方差，这些模型进行比较，使用概率密度的发散性措施。然后，使用基于这些相似性度量的合适的聚类算法，可以定义图案。快速的时间序列分割和建模技术，然后允许在新的时间序列中的非常低的运行时间的先前定义的图案的在线检测。SwiftMotif适用于实时应用，考虑了与某些图案的出现相关的不确定性，例如，由于噪声，并考虑局部变化（即，均匀缩放）。本文重点介绍SwiftMotifs的数学基础和特性的演示--特别是准确性和运行时间--使用一些人工和真实的基准时间序列。
This article presents SwiftMotif, a novel technique for on-line motif detection in time series. With this technique, frequently occurring temporal patterns or anomalies can be discovered, for instance. The motif detection is based on a fusion of methods from two worlds: probabilistic modeling and similarity measurement techniques are combined with extremely fast polynomial least-squares approximation techniques. A time series is segmented with a data stream segmentation method, the segments are modeled by means of normal distributions with time-dependent means and constant variances, and these models are compared using a divergence measure for probability densities. Then, using suitable clustering algorithms based on these similarity measures, motifs may be defined. The fast time series segmentation and modeling techniques then allow for an on-line detection of previously defined motifs in new time series with very low run-times. SwiftMotif is suitable for real-time applications, accounts for the uncertainty associated with the occurrence of certain motifs, e.g., due to noise, and considers local variability (i.e., uniform scaling) in the time domain. This article focuses on the mathematical foundations and the demonstration of properties of SwiftMotif—in particular accuracy and run-time—using some artificial and real benchmark time series.