Segmental Semi-Markov Model Based Online Series Pattern Detection Under Arbitrary Time Scaling

Efficient online detection of similar patterns under arbitrary time scaling of a given time sequence is a challenging problem in the real-time application field of time series data mining. Some methods based on sliding window have been proposed. Although their ideas are simple and easy to realize, their computational loads are very expensive. Therefore, model based methods are proposed. Recently, the segmental semi-Markov model is introduced into the field of online series pattern detection. However, it can only detect the matching sequences with approximately equal length to that of the query pattern. In this paper, an improved segmental semi-Markov model, which can solve this challenging problem, is proposed. And it is successfully demonstrated on real data sets.