The event prediction task learns the dynamic changes of past events and infers the upcoming ones. This task helps people to understand how events evolve in our real world and make rapid, accurate, and efficient reactions in emergencies. The complex relation of events and entities (events participants) changes dynamically over time. The current solutions of event prediction have not fully exploited such event temporal dependency and entity relation dependency. In addition, an event may relate to a considerable number of entities. Among such related entities, only a few of them are the key participants in driving the evolution of the event. Previous works fail to emphasize such key entities and limit the non-related entities. In this paper, we introduce a novel gating and attention mechanism and propose a novel Temporal spAtial dyNamic Graph mOdel (TANGO) that is composed of a graph model (based on Graph Convolutional Network with gated and attention mechanisms) and a sequential model (based on Temporal Convolutional Network). TANGO is able to model the event temporal dependency and entity relation dependency simultaneously; learn the representation of events relations; control the aggregation of relational information among entities with varying degrees of mutual influence; support long effective historical sizes. We demonstrate the validity and effectiveness of our approach on three different datasets (i.e., ICEWS18, GDELT, and ICEWS14). The result shows almost 6.2% MRR improvement and 3.9% Hits@10 improvement over the previous state-of-the-art.