The field of predictive analytics is moving towards the development of more sophisticated and reliable methods for forecasting and decision-making in complex systems. Researchers are focusing on improving the accuracy and efficiency of predictive models, particularly in applications such as airspace operations, energy management, and time series forecasting. Innovative techniques, including ensemble models, conformal prediction, and ambiguity rejection mechanisms, are being explored to address the challenges of uncertainty and reliability in predictive analytics. These advances have the potential to significantly impact various industries, from aviation to renewable energy, by enabling more informed decision-making and optimized operations. Noteworthy papers in this area include:

• A Predictive Services Architecture for Efficient Airspace Operations, which presents a novel data processing and predictive services architecture for forecasting future airspace system states.
• Dual-Splitting Conformal Prediction for Multi-Step Time Series Forecasting, which proposes a new conformal prediction approach for multi-step time series forecasting and demonstrates its effectiveness in reducing carbon emissions through predictive optimization of data center operations.