Abstract: Clustering a set of univariate time series based on their dynamics is a popular problem that can be widely found in many research areas Because the common clustering algorithm can’t directly be used to resolve this problem, a new approach to grouping time series is proposed This approach firstly models each time series as a Markov chain that captures the dynamic character of the time series, and then gets the clustering result to these time series by clustering the Markov chains Using this method, two different data sets are clustered, in which, one is real data, and the other is artificial data By qualitative and quantitative analysis, it is proved that both of the clustering results are good