The field of time series forecasting is rapidly evolving, with a focus on improving accuracy and robustness in predicting complex and chaotic systems. Recent research has explored the use of deep learning models, such as convolutional and recurrent neural networks, in combination with data augmentation techniques to enhance forecasting performance. Additionally, there is a growing interest in developing models that can adapt to changing patterns and relationships in data, such as those using reinforcement learning and generative world models. Another key area of research is the development of methods that can effectively handle extrapolation and long-range forecasting, which remains a challenging task for many current models. Noteworthy papers in this area include: The paper on Extreme value forecasting using relevance-based data augmentation with deep learning models, which presents a novel framework for forecasting extreme values using deep learning models and data augmentation techniques. The paper on Why Cannot Neural Networks Master Extrapolation, which identifies a fundamental property characterizing the ability of statistical learning models to predict outside of their training domain and provides insights for designing next-generation forecasting models.