New segment method of temporal data for outlier detection
Weihua He · Jisuanji gongcheng yu sheji · 2007
General approaches for outlier detection need to divide temporal data into sub-sequences so as to reduce complexity.The existing methods divide temporal data by application,which is not available on some occasions.A new segment method based on the properties of temporal data is proposed,which divided temporal data by combining important point with their breaking factor(BF).Microsoft stock price series are used for testing.The results show that the segment method is simple,intuitive,independent of application,and outperforms relevant method.