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Multivariate Forecasting Evaluation: On Sensitive and Strictly Proper Scoring Rules by Ziel, Florian; Berk, Kevin is a scholarly article available to read on EtoBox.

What is Multivariate Forecasting Evaluation: On Sensitive and Strictly Proper Scoring Rules about?

In recent years, probabilistic forecasting is an emerging topic, which is why there is a growing need of suitable methods for the evaluation of multivariate predictions. We analyze the sensitivity of the most common scoring rules, especially regarding quality of the forecasted dependency structures. Additionally, we propose scoring rules based on the copula, which uniquely describes the dependency structure for every probability distribution with continuous marginal distributions. Efficient estimation of the considered scoring rules and evaluation methods such as the Diebold-Mariano test are discussed. In detailed simulation studies, we compare the performance of the renowned scoring rules and the ones we propose. Besides extended synthetic studies based on recently published results we also consider a real data example. We find that the energy score, which is probably the most widely used multivariate scoring rule, performs comparably well in detecting forecast errors, also regarding dependencies. This contradicts other studies. The results also show that a proposed copula score provides very strong distinction between models with correct and incorrect dependency structure. We clo

Author

Ziel, Florian; Berk, Kevin

Published

2019

Language

EN