Uncertain continuous time series Top-K anomaly detection method
Meng Fan-ron · Jisuanji yingyong yanjiu · 2014
Aimed at the problem that the noise data influence on the anomaly detection results for time series,this paper put forward a kind of uncertain continuous time series Top-K anomaly detection algorithm. Based on the typical time series anomaly detection method,it dealed with time series discord as interval,and structured density functions of uniform distribution. Then it combined with the uncertain scores Top-K technology to implement Top-K ranking on uncertain time series data with noise and unknown distribution. Experiment tests on simulated data and real data,this proposed algorithm has increased significantly in anomaly detection accuracy than traditional time series detection method. Although the method has increased in computing time,this paper put forward the corresponding optimization strategy and improved on computing time obviously when the K value was large. It verifies the effectiveness of the algorithm.