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Probabilistic Forecasting

机译：概率预测

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摘要

A probabilistic forecast takes the form of a predictive probability distribution over future quantities or events of interest. Probabilistic forecasting aims to maximize the sharpness of the predictive distributions, subject to calibration, on the basis of the available information set. We formalize and study notions of calibration in a prediction space setting. In practice, probabilistic calibration can be checked by examining probability integral transform (PIT) histograms. Proper scoring rules such as the logarithmic score and the continuous ranked probability score serve to assess calibration and sharpness simultaneously. As a special case, consistent scoring functions provide decision-theoretically coherent tools for evaluating point forecasts. We emphasize methodological links to parametric and nonparametric distributional regression techniques, which attempt to model and to estimate conditional distribution functions; we use the context of statistically postprocessed ensemble forecasts in numerical weather prediction as an example. Throughout, we illustrate concepts and methodologies in data examples.
机译：概率预测采取对未来数量或感兴趣事件的预测概率分布的形式。概率预测旨在基于可用信息集，在经过校准的情况下最大化预测分布的清晰度。我们对预测空间设置中的校准概念进行形式化和研究。实际上，可以通过检查概率积分变换（PIT）直方图来检查概率校准。正确的评分规则（例如对数分数和连续排名的概率分数）可用于同时评估校准和清晰度。在特殊情况下，一致的评分功能为评估点预测提供了决策理论上一致的工具。我们强调方法论与参数和非参数分布回归技术的联系，这些方法试图对条件分布函数进行建模和估计。我们以数值天气预报中的统计后处理集合预报为背景。在整个过程中，我们在数据示例中说明概念和方法。

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