非平稳性下时间序列模式的评价及其在区域气候模式比较中的应用
Evaluation of time series models under non-stationarity with application to the comparison of regional climate models
摘要：
不同的学科追求的目标是开发尽可能准确地描述某些现象的模型。气候学就是一个很好的例子，它模拟了气候的时间演变。为了比较和改进不同的模型，一个公平的模型评估方法是必不可少的。由于一种现象的模型和预测通常与不确定性有关，适当的评分规则是考虑这种不确定性的工具，是模型评估的适当选择。然而，在非平稳性的存在下，这样的模型评估变得具有挑战性，因为感兴趣的现象的特征发生变化。我们提供的方法模型评估的背景下，非平稳时间序列。我们的方法假设平稳的时间序列在较短的移动时间窗口。基于变点分析选择的这些移动窗口用于表征对应时间实例的现象/模型的不确定性。这导致了移动分数的概念，允许对模型性能进行时间评估。所提出的方法的优点是说明在模拟和案例研究。
英文摘要：
Different disciplines pursue the aim to develop models which characterize certain phenomena as accurately as possible. Climatology is a prime example, where the temporal evolution of the climate is modeled. In order to compare and improve different models, methodology for a fair model evaluation is indispensable. As models and forecasts of a phenomenon are usually associated with uncertainty, proper scoring rules, which are tools that account for this kind of uncertainty, are an adequate choice for model evaluation. However, under the presence of non-stationarity, such a model evaluation becomes challenging, as the characteristics of the phenomenon of interest change. We provide methodology for model evaluation in the context of non-stationary time series. Our methodology assumes stationarity of the time series in shorter moving time windows. These moving windows, which are selected based on a changepoint analysis, are used to characterize the uncertainty of the phenomenon/model for the corresponding time instances. This leads to the concept of moving scores allowing for a temporal assessment of the model performance. The merits of the proposed methodology are illustrated in a simulation and a case study.
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