The recent developments in the research area of time series forecasting and analysis have shown a significant shift towards leveraging large language models (LLMs) and deep learning techniques. The field is increasingly focusing on integrating LLMs with traditional forecasting methods to enhance prediction accuracy and computational efficiency. This integration is particularly evident in the use of LLMs for generating and fine-tuning models, as well as for incorporating unstructured textual data into forecasting pipelines. The advancements are not limited to model generation but also extend to the development of hybrid models that combine the strengths of different algorithms, such as Gaussian Process Regression with Support Vector Regression, to improve out-of-sample predictions. Additionally, there is a growing interest in the application of pre-trained large time series models for industrial contexts, highlighting their potential for swift deployment and few-shot learning capabilities. The research also underscores the importance of considering real-world constraints and market conditions when developing forecasting models, as seen in studies focusing on virtual bidding in electricity markets and the impact of economic indicators on portfolio management. Overall, the field is moving towards more sophisticated and context-aware models that can adapt to varying data conditions and market dynamics.