The field of predictive modeling is experiencing significant growth, driven by the increasing availability of large datasets and advances in machine learning techniques. Researchers are exploring innovative approaches to improve forecasting accuracy in various domains, including energy and finance. A key trend is the integration of external data sources, such as weather and sentiment scores, to enhance model performance. Another area of focus is the development of adaptive models that can dynamically respond to changing conditions. Noteworthy papers include:

• One that proposes a hybrid deep learning and machine learning model for cryptocurrency price prediction, demonstrating improved accuracy over traditional methods.
• Another that introduces a meta-representation framework for adaptive probabilistic load forecasting, achieving state-of-the-art results with limited additional overhead.