In order to cluster time series data, this paper presents spectral clustering method based on independent component analysis for time series and gives some theoretical interpretations for feature extraction and dimension reduction of time series data by using independent component analysis. The method includes two steps. In the rst step, it conducts feature extraction and dimension reduction of time series data by applying independent component analysis. In the second step, it clusters time series data using multiway normalized cut spectral clustering algorithm. Consequently, a new feature-based time series clustering method is derived. With the purpose of validating feasibility and e ectiveness of the presented method, the method is used to analyze the simulation time series data and the real world stock time series data and much better numerical results are derived.