Temporal graphs are extensively employed to represent evolving networks, finding applications across diverse fields such as transportation systems, social networks, and biological networks.Temporal Graph Networks (TGNs) build upon these graphs to model and learn from temporal dependencies in dynamic networks.A significant aspect of enhancing the performance of TGNs lies in effective data augmentation, which helps in better capturing the underlying patterns within temporal graphs while ensuring robustness to variations. However, existing data augmentation strategies for temporal graphs are largely heuristic and hand-crafted, which may alter the inherent semantics of temporal graphs, thereby degrading the performance of downstream tasks. To address this, we propose two simple yet effective data augmentation strategies, specifically tailored within the representation space of TGNs, targeting both the graph topology and the temporal axis. Through experiments on future link prediction and node classification tasks, we demonstrate that the integration of our proposed augmentation methods significantly amplifies the performance of TGNs, outperforming state-of-the-art methods.