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An introduction to multivariate probabilistic forecast evaluation

Energy and AI 2021年第2期4卷 119-135页

作者： Mathias Blicher Bjerregard Jan Kloppenborg Møller Henrik Madsen Technical University of Denmark Department of Applied Mathematics and Computer Denmark

probabilistic forecasting is becoming increasingly important for a wide range of applications,especially for en-ergy systems such as forecasting wind power production.A need for proper evaluation of probabilistic forecasts follows naturally with this,because evaluation is the key to improving the *** plenty of excellent reviews and research papers on probabilistic forecast evaluation already exist,we find that there is a need for an introduction with some practical *** particular,many forecast scenarios in energy systems are inher-ently multivariate,and while univariate evaluation methods are well understood and documented,only limited and scattered work has been done on their multivariate *** paper therefore contains a review of a selected set of probabilistic forecast evaluation methods,primarily scoring rules,as well as practical sections that explain how these methods can be calculated and *** three case studies featuring simple autore-gressive models,stochastic differential equations and real wind power data,we implement,apply and discuss the logarithmic score,the continuous ranked probability score and the variogram score for forecasting problems of varying ***,the advantages and disadvantages of the three scoring rules are highlighted,and this provides a significant step towards deciding on an evaluation method for a given multivariate forecast scenario including forecast scenarios relevant for energy systems.

关键词： probabilistic forecast evaluation Multivariate scoring rules Wind power forecast Ensemble forecast Time series analysis

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