Online Series Pattern Detection Based on Advanced Segmental Semi-Markov Model
Ling Guang · 2007
Efficient online detection of similar patterns under arbitrary time scaling is a challenging problem in time series data mining.A model-matching based segmental semi-Markov model is improved by introducing offset distribution,amplitude difference distribution and pre-pattern state.It overcomes the parameter estimation difficulty and the lack of robustness.The experimental results demonstrate that the advanced segmental semi-Markov model could rapidly and precisely detect scaling similar patterns under arbitrary time.