Time series representation for anomaly detection

M. Leng, Xinsheng Lai, Guolv Tan, Xiaohui Xu
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引用次数: 22

Abstract

Anomaly detection in time series has attracted a lot of attention in the last decade, and is still a hot topic in time series mining. However, time series are high dimensional and feature correlational, directly detecting anomaly patterns in its raw format is very expensive, in addition, different time series may have different lengths of anomaly patterns, and usually, the lengths of anomaly patterns is unknown. This paper presents a new conception key point and an algorithm of seeking key points, the algorithm uses key points to rerepresent time series and still preserves its fundamental characteristics. Variable length method was used to segment re-represented time series into patterns and calculate anomaly scores of patterns. Anomaly patterns are identified by their anomaly scores automatically. The effectiveness of representational algorithm and anomaly detecting algorithm are demonstrated with both synthetic and standard datasets, and the experimental results confirm that our methods can identify anomaly patterns with different lengths and improve the speed of detecting algorithm greatly.
异常检测的时间序列表示
时间序列异常检测在近十年来引起了人们的广泛关注,目前仍然是时间序列挖掘领域的一个热点问题。然而,由于时间序列具有高维性和特征相关性,直接以其原始格式检测异常模式的成本非常高,而且不同的时间序列可能具有不同长度的异常模式,通常异常模式的长度是未知的。提出了一种新的关键点概念和寻找关键点的算法,该算法利用关键点来表示时间序列,同时保持了时间序列的基本特征。采用变长法将重新表示的时间序列分割成模式,并计算模式的异常分数。异常模式由异常分数自动识别。在合成数据集和标准数据集上验证了表征算法和异常检测算法的有效性,实验结果表明,我们的方法可以识别不同长度的异常模式,大大提高了检测算法的速度。
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