Collections of time series formed via aggregation are prevalent in many fields. These are commonly referred to as hierarchical time series and may be constructed cross-sectionally across different variables, temporally by aggregating a single series at different frequencies, or even generalised beyond aggregation as time series that respect linear constraints. When forecasting such time series, a desirable condition is for forecasts to be coherent: to respect the constraints. The past decades have seen substantial growth in this field with the development of reconciliation methods that ensure coherent forecasts and improve forecast accuracy. This paper serves as a comprehensive review of forecast reconciliation and an entry point for researchers and practitioners dealing with hierarchical time series. The scope of the article includes perspectives on forecast reconciliation from machine learning, Bayesian statistics and probabilistic forecasting, as well as applications in economics, energy, tourism, retail demand and demography.

中文翻译：

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预测调节：回顾

通过聚合形成的时间序列集合在许多领域都很普遍。这些通常被称为分层时间序列，并且可以跨不同变量横截面构建，通过以不同频率聚合单个序列来临时构建，或者甚至概括为超越聚合的尊重线性约束的时间序列。在预测此类时间序列时，理想的条件是预测保持一致：尊重约束。过去几十年来，随着调节方法的发展，确保预测的连贯性并提高预测准确性，该领域取得了长足的发展。本文是对预测调节的全面回顾，也是处理分层时间序列的研究人员和从业者的切入点。本文的范围包括机器学习、贝叶斯统计和概率预测的预测协调的观点，以及在经济、能源、旅游、零售需求和人口学中的应用。