The recent developments in the research area of time series forecasting (TSF) have shown a significant shift towards enhancing model interpretability, robustness, and scalability. Researchers are increasingly focusing on disentangled representations and multi-scale feature extraction to improve forecasting accuracy, particularly in high-dimensional and noisy data environments. The integration of contrastive learning and adaptive noise augmentation strategies is emerging as a key technique to handle data sparsity and noise, enabling models to better capture complex temporal patterns. Additionally, there is a growing emphasis on the practical applicability of TSF models in real-world scenarios, such as smart manufacturing and urban road reconstruction projects, where the ability to forecast with minimal hyperparameter tuning and computational resources is highly valued. Notably, the field is witnessing advancements in multivariate time series forecasting, where models are designed to explicitly map historical to future series and extract long-range dependencies, enhancing both accuracy and interpretability. These trends collectively suggest a move towards more efficient, interpretable, and robust TSF models that can be readily deployed in various high-stakes applications.