Being able to infer possible events related to a specific target is critical to natural language processing. One challenging task in this line is \emph{event sequence prediction}, which aims at predicting a sequence of events given a goal. Currently existing approach models this task as a \emph{statistical induction} problem, to predict a sequence of events by exploring the similarity between the given goal and the known sequences of events. However, this statistical based approach is complex and predicts a limited variety of events. At the same time this approach ignores the rich knowledge of external events that is important for predicting event sequences. In this paper, in order to predict more diverse events, we first reformulate the event sequence prediction problem as a sequence generation problem. Then to leverage external event knowledge, we propose a three-stage model including augmentation, retrieval and generation. Experimental results on the event sequence prediction dataset show that our model outperforms existing methods, demonstrating the effectiveness of the proposed model.