Coherent Forecast

Coherent forecasting aims to generate accurate probabilistic predictions across multiple hierarchical levels (e.g., spatial or temporal aggregations), ensuring consistency between forecasts at different scales. Recent research focuses on developing novel neural network architectures, including those based on diffusion models, factor models, and multivariate mixture networks, to achieve this coherence, often employing techniques like hierarchical reconciliation and composite likelihood optimization. These advancements improve the accuracy and reliability of forecasts in diverse applications such as weather prediction, energy management, and supply chain planning, leading to better decision-making in complex systems.