Game-theoretically Optimal Reconciliation of Contemporaneous Hierarchical Time Series Forecasts
Game-theoretically Optimal Reconciliation of Contemporaneous Hierarchical Time Series Forecasts
复制标题
同时期分层时间序列预测的博弈论最优协调
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Jairo Cugliari
中科院分区：
文献类型：
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作者：
T. Erven;Jairo Cugliari
在层次时间序列预测中，利用多个时间序列之间的层次关系进行更好的预测。这种层次关系意味着一个或多个聚合一致性约束，该系列是已知的满足。因此，许多现有的方法，例如自下而上或自上而下的预测，都试图以保证预测总体一致的方式实现这一目标。我们建议将HTS的问题分为两个独立的步骤：第一个是提出最好的可能的预测的时间序列，而不必担心总的一致性，然后使用一个和解程序，使预测总的一致性。我们引入了一个博弈论优化（GTOP）和解方法，这是保证只提高任何给定的预测集。这为预测的构建开辟了新的可能性。例如，没有必要假设底层预测是无偏的，可以通过对底层预测和可能仅在总体水平上可用的其他协变量进行回归来构建总体预测。我们说明了我们的方法的好处，模拟数据和真实的电力消耗数据。
In hierarchical time series (HTS) forecasting, the hierarchical relation between multiple time series is exploited to make better forecasts. This hierarchical relation implies one or more aggregate consistency constraints that the series are known to satisfy. Many existing approaches, like for example bottom-up or top-down forecasting, therefore attempt to achieve this goal in a way that guarantees that the forecasts will also be aggregate consistent. We propose to split the problem of HTS into two independent steps: first one comes up with the best possible forecasts for the time series without worrying about aggregate consistency; and then a reconciliation procedure is used to make the forecasts aggregate consistent. We introduce a Game-Theoretically OPtimal (GTOP) reconciliation method, which is guaranteed to only improve any given set of forecasts. This opens up new possibilities for constructing the forecasts. For example, it is not necessary to assume that bottom-level forecasts are unbiased, and aggregate forecasts may be constructed by regressing both on bottom-level forecasts and on other covariates that may only be available at the aggregate level. We illustrate the benefits of our approach both on simulated data and on real electricity consumption data.