The recent developments in the research area of time series forecasting and financial market analysis have shown a significant shift towards leveraging advanced machine learning techniques, particularly in the context of multimodal data integration and causal discovery. Researchers are increasingly focusing on the fusion of textual, numerical, and temporal data to enhance predictive accuracy in both financial markets and environmental monitoring. The use of large language models (LLMs) and transformer architectures has become prominent, with innovations such as Prion-ViT and CausalStock demonstrating the potential of these models in capturing complex dependencies and causal relationships. Additionally, the field is witnessing a renaissance in architectural diversification, with hybrid models and diffusion models emerging as strong contenders. The integration of retrieval-augmented generation (RAG) techniques is also gaining traction, suggesting that dynamic and event-driven data can benefit significantly from context-aware forecasting methods. These advancements not only improve the precision of predictions but also offer new avenues for explainability and real-time decision-making in high-stakes environments such as financial trading and environmental safety. Notably, the development of fully automated forecasting frameworks and the exploration of dual-valued functions in causal emergence are pushing the boundaries of what is possible in these domains, promising more efficient and robust solutions for complex time series problems.