Forecast Error

Forecast error, the discrepancy between predicted and actual values, is a central challenge across diverse fields, driving research to improve prediction accuracy and understand error sources. Current efforts focus on identifying influential variables impacting forecast accuracy (e.g., using ranking techniques and incorporating external factors), comparing human judgment with AI models like LLMs, and optimizing the trade-off between computational efficiency and error through techniques like data reduction and adaptive retraining with algorithms such as XGBoost, Transformers, and Hoeffding Trees. Understanding and mitigating forecast error is crucial for improving decision-making in areas ranging from weather prediction and resource management to financial forecasting and risk assessment.