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The problem of time series classification has drawn intensive attention from the data mining community. Conventional time series model may be unsuitable for multivariate motion time series because of the large volume of the data, highly correlated dimensions and rapid growth nature. In this paper, we propose C3M, an effective classification model for motion time series classification, which consists of segmentation, dimension ranking and selection, and classification. We propose new segmentation and dimension selection scheme that reduce the storage volume but keep enough valuable information and correlation between different dimensions. Experimental results show that C3M achieves significant performance improvements in terms of both classification accuracy and execution time over conventional schemas.