The field of time series forecasting is moving towards more generalizable and adaptable models. Recent developments have focused on improving the accuracy and efficiency of forecasting systems, particularly in scenarios where data is limited or uncertain. The use of pre-trained models, attention mechanisms, and resolution-aware retrieval strategies have shown promising results in achieving state-of-the-art performance across various benchmarks. Notably, the integration of fairness and epidemic dynamics with vaccine logistics has led to superior outcomes in epidemic forecasting. Furthermore, the development of lightweight foundation models has made it possible to deploy forecasting systems in resource-constrained environments. Some noteworthy papers in this area include: Chronos-2, which delivers state-of-the-art performance across three comprehensive benchmarks. Resolution-Aware Retrieval Augmented Zero-Shot Forecasting, which significantly outperforms traditional forecasting methods in microclimate forecasting. Proactive and Fair Epidemic Resource Allocation, which prevents more than 2 million infections and 30,000 deaths in six months. A Conditional Diffusion Model, which achieves a relative Mean Absolute Error of 0.94% in battery capacity degradation forecasting. A Climate-Aware Deep Learning Framework, which reliably outperforms statistical baselines in RSV forecasting. SEMPO, which reduces pre-training data scale and model size while achieving strong generalization. QKCV Attention, which enhances forecasting accuracy of attention-based models across diverse real-world datasets.