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In this paper, we study the dynamic transfer learning problem involving adaptive knowledge transfer from a static source domain to a time evolving target domain. One major challenge is the time evolving relatedness of the source domain and the current target domain as the target domain evolves over time. To address this challenge, we derive a generic error bound on the current target domain with flexible domain discrepancy measures. Moreover, we propose a label-informed -divergence to measure the shift of joint data distributions (over input features and output labels) across domains. The resulting tighter error bound with -divergence motivates us to develop a novel dynamic transfer learning algorithm TransLATE. Empirical results on various data sets confirm the effectiveness of our proposed algorithm in modeling the time evolving target domain.