基于重要边缘点的时间序列异常模式检测算法

在分析边缘算子的思想和现有时间序列模式表示方法基础上，将边缘点方法和重要点方法相结合，提出了基于重要边缘点的时间序列模式表示算法。算法按各观测点的边缘化程度，提取重要的边缘点将时间序列分成多个子线段，通过分析直线段之间的相似性，发现异常的序列模式。从理论和实验两方面对算法进行了分析和验证，结果表明，算法复杂度较低，模式表示误差小，能够满足大规模时间序列数据模式表示的要求。

Abnormal pattern detection algorithm for time series based on important edge points

Based on analyzing thoughts and concepts of edge operator and advantages and disadvantages of existing pattern representation of time series algorithms,combing with edge point method and important point method, a new algorithm, named pattern representation algorithm based on important edge points of time series, is proposed and analyzed.According to the marginalization of observation point, time series is divided into a plurality of sub segments by extracting the important edge point. Then, the straight line segment that abnormal values is higher,also called abnormal sequence pattern, is found by the analysis of its similarity. The algorithm is analyzed and verified theoretically and in experiments. It is proved that the new algorithm has low complexity,small pattern representation error and can satisfy the pattern representation requirements of mass time series data.