The recent advancements in the field of generative modeling and time series forecasting have shown a significant shift towards integrating probabilistic approaches with diffusion-based techniques. Researchers are increasingly leveraging Stochastic Differential Equations (SDEs) and diffusion processes to enhance the accuracy and robustness of models in capturing complex spatiotemporal dependencies. This trend is particularly evident in applications such as traffic forecasting and pedestrian flow analysis, where the incorporation of graph neural networks and novel denoising score models has led to state-of-the-art performance. Additionally, the use of multi-temporal data for tasks like crack segmentation in concrete structures has demonstrated substantial improvements in model accuracy and consistency, highlighting the potential of temporal information in enhancing deep learning models. Overall, the field is moving towards more sophisticated, continuous, and probabilistic modeling approaches that promise to advance various domains by effectively managing uncertainty and improving prediction capabilities.