Time Series Classification Algorithm Based on Linear Segmentation and HMM
Li Xiong · 2011
The multi-segment linear (MSL) feature of the time series are collected, and a time series classification algorithm is proposed, which consists of derivative estimation function, linear segmentation method and DDHMM model (base on HMM). Firstly, the derivative estimation function and the linear segmentation method can be used together to detect the MSL feature. If they are matched, time series can be converted into observed sequence with a special structure. Next, the training observed sequences can be used to train DDHMM models. After training, the time series are classified through comparing the probability value of testing observed sequences generated by each model. The experimental results show that the proposed algorithm has a high accuracy when classifying the time series that match the MSL feature, and it has good performance in the classification on the UCI dataset and the actual projects.