Recent advancements in time series forecasting are increasingly focused on enhancing model efficiency and accuracy while addressing the complexities of real-world data. New frameworks are emerging that prioritize long-term forecasting stability, such as those that utilize look-ahead augmentation and self-corrective refinement to improve predictive consistency. Additionally, linear models are gaining traction for their ability to reduce computational complexity, enabling faster inference without sacrificing performance. The introduction of global temporal retrieval mechanisms allows models to capture long-range dependencies more effectively, while dual-path architectures are being explored to separate autoregressive dynamics from cross-dimensional interactions, enhancing overall accuracy. Furthermore, the integration of mixture-of-experts approaches is proving effective in managing diverse temporal patterns, improving robustness against noise, and facilitating uncertainty quantification. These developments suggest a shift towards more interpretable and efficient forecasting solutions that can better serve industries reliant on accurate predictions, such as finance, healthcare, and education.