Time Series Representation for Anomaly Detection
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.
time series key points time series representation anomaly patterns
Mingwei Leng Xinsheng Lai Guolv Tan Xiaohui Xu
Department of mathematics and computer Shangrao Normal University Shangrao, 334000, China
国际会议
北京
英文
1289-1293
2009-08-08（万方平台首次上网日期，不代表论文的发表时间）