An effective distance metric is essential for time series clustering. To improve the performance of time series clustering, various methods of metric learning can be applied to generate a proper distance metric from the data. However, the existing metric learning methods overlook the characteristics of time series. And for time series, it is difficult to obtain side information, such as pairwise constraints, for metric learning. In this paper, a method for distance metric learning based on side information autogeneration for time series (SIADML) is proposed. In this method, dynamic time warping (DTW) distance is used to measure the similarity between two time series and generate pairwise constraints automatically. The metric which is learned from the pairwise constraints can preserve the neighbor relationship of time series as much as possible. Experimental results on benchmark datasets demonstrate that the proposed method can effectively improve the performance for time series clustering.