Forecast Reconciliation

Forecast reconciliation addresses the problem of inconsistencies in hierarchical time series forecasts, where forecasts at different aggregation levels (e.g., total sales versus sales by region) may not sum correctly. Current research focuses on probabilistic methods, often integrating reconciliation directly into deep learning frameworks or employing Bayesian approaches for improved accuracy and coherence. These advancements are crucial for various applications, such as optimizing vaccine supply chains, where accurate and consistent forecasts across different levels of granularity are essential for effective resource allocation and decision-making. The field is actively exploring optimal reconciliation algorithms, including those based on minimum trace, weighted least squares, and Kullback-Leibler divergence regularization.