The online pattern detection technology is an important part of the time series analysis, and some methods have been proposed, in which distance based window-sliding is popularly applied. For window-sliding, Euclidean distance and dynamic time warping (DTW) are always used as subsequence matching, but they have the drawbacks of sensitivity and expensive computational load respectively. Recently, the model based method is introduced into the field of online pattern detection, especially, the segmental semi-Markov model shows better performance than sliding methods in many aspects. However, the resolution of the model is limited. In this paper a hybrid online series pattern detection algorithm, which combines the distance based method and the model based method, is proposed. It is successfully demonstrated on real data sets, including financial and medical data.