The field of time series forecasting is witnessing significant advancements with the introduction of novel transformer-based architectures that can effectively model both temporal and inter-channel dependencies. These developments have far-reaching implications for multivariate time-series forecasting, particularly in applications such as renewable energy prediction and global air pollution forecasting. Noteworthy papers in this area include the Sentinel model, which achieves state-of-the-art performance in time series forecasting by leveraging a multi-patch attention mechanism. Another significant contribution is the probabilistic net load forecasting framework utilizing an enhanced conditional diffusion model, which demonstrates exceptional potential in uncertainty quantification for scenario forecasting. Additionally, the WeatherMesh-3 system showcases a fast and accurate operational global weather forecasting system, pushing the boundaries of machine learning-based weather prediction. The Seasonal-Periodic Decomposition Network (SPDNet) is also a notable mention, as it effectively captures intricate temporal dynamics in residential electricity demand forecasting.