The field of time series forecasting is rapidly evolving, with a growing focus on developing more accurate and efficient models. Recent research has highlighted the limitations of traditional models, including their tendency to perceive non-existent patterns and their vulnerability to concept drift and temporal shifts. In response, researchers are exploring new approaches, such as the use of large language models, covariate-aware adaptation, and selective representation spaces. These innovative methods have shown promising results, including improved forecasting accuracy and reduced parameter requirements. Notably, papers such as SVTime and CoRA have introduced novel frameworks for time series forecasting, leveraging the strengths of large vision models and foundation models to achieve state-of-the-art performance. Meanwhile, papers like Lifting Manifolds to Mitigate Pseudo-Alignment in LLM4TS and Toward Reasoning-Centric Time-Series Analysis are pushing the boundaries of time series analysis, emphasizing the importance of causal structure, explainability, and human-aligned understanding. Overall, the field is moving towards more robust, adaptable, and interpretable models that can effectively handle the complexities of real-world time series data.