Sequential models are designed to learn sequential patterns in data based on the chronological order of user interactions. However, they often ignore the timestamps of these interactions. Incorporating time is crucial because many sequential patterns are time-dependent, and the model cannot make time-aware recommendations without considering time. This article demonstrates that providing a rich representation of time can significantly improve the performance of sequential models. The existing literature treats time as a one-dimensional time-series obtained by quantizing time. In this study, we propose treating time as a multi-dimensional time-series and explore representation learning methods, including a kernel based method and an embedding-based algorithm. Experiments on multiple datasets show that the inclusion of time significantly enhances the model’s performance, and multi-dimensional methods outperform the one-dimensional method by a substantial margin.