Sequential recommendation, which aims to leverage users' historical behaviors to predict their next interaction, has become a research hotspot in the field of recommendation. Time is one of the important contextual information for interaction. However, most previous works only use time information as a model feature or time prediction as an auxiliary task and ignore the duality between sequential recommendation task and time prediction task. Compared with the method of sharing parameters in multi-task learning, this paper proposed a dual learning framework to jointly model two tasks and incorporate the probabilistic dual properties between them in the training stage. In addition, we design an appropriate base model for each task. Finally, experiments on two public datasets demonstrated the effectiveness of the proposed dual learning framework in sequential recommendation scenarios.