The field of time series forecasting is moving towards more advanced and innovative techniques, with a focus on improving model performance and generalization. Recent developments have seen the integration of self-supervised learning, hypernetworks, and attention mechanisms to enhance forecasting accuracy. These approaches have shown promising results, particularly in handling complex temporal dependencies and out-of-distribution scenarios. Notable papers include: IBMA, which proposes a novel imputation-based mixup augmentation approach that consistently enhances performance across various forecasting models. PE-TSFM, which achieves strong out-of-distribution generalization for power converter health monitoring through a domain-specific time-series foundation model. HN-MVTS, which improves multivariate time series forecasting by integrating a hypernetwork-based generative prior with arbitrary neural network forecasting models. EMAformer, which enhances the Transformer architecture for time series forecasting through an auxiliary embedding suite, achieving state-of-the-art performance on real-world benchmarks.