Multivariate Forecasting Evaluation: On Sensitive and Strictly Proper Scoring Rules

Understand

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.