Coevolving multiple time series are ubiquitous and naturally appear in a variety of high-impact applications, ranging from environmental monitoring, computer network traffic monitoring, motion capture, to physiological signal in health care and many more. In many scenarios, the multiple time series data is often accompanied by some contextual information in the form of networks. In this paper, we refer to such multiple time series, together with its embedded network as a network of coevolving time series. In order to unveil the underlying patterns of a network of coevolving time series, we propose DCMF, a dynamic contextual matrix factorization algorithm. The key idea is to find the latent factor representation of the input time series and that of its embedded network simultaneously. Our experimental results on several real datasets demonstrate that our method (1) outperforms its competitors, especially when there are lots of missing values; and (2) enjoys a linear scalability w.r.t. the length of time series.