数据驱动的降雨时间序列预测及SOC模型
Data-Driven Prediction of Thresholded Time Series of Rainfall and SOC models
摘要：
我们研究了事件的发生，受阈值，在一个有代表性的SOC沙堆模型和高分辨率的降雨数据。在这两个系统中的可预测性分析的决策变量敏感的事件聚类的装置，和质量的预测评价的受试者工作特性（ROC）的方法。在SOC沙堆模型的情况下，随着阈值的增加，静止时间分布的缩放导致极端事件的可预测性增加。标度理论使我们能够理解预测过程的所有细节，并推断出大多数极端事件的ROC曲线的形状。对于降雨数据，安静时间分布不缩放高阈值，这意味着相应的ROC曲线不能直接相关的阈值较低。
英文摘要：
We study the occurrence of events, subject to threshold, in a representative SOC sandpile model and in high-resolution rainfall data. The predictability in both systems is analyzed by means of a decision variable sensitive to event clustering, and the quality of the predictions is evaluated by the receiver operating characteristics (ROC) method. In the case of the SOC sandpile model, the scaling of quiet-time distributions with increasing threshold leads to increased predictability of extreme events. A scaling theory allows us to understand all the details of the prediction procedure and to extrapolate the shape of the ROC curves for the most extreme events. For rainfall data, the quiet-time distributions do not scale for high thresholds, which means that the corresponding ROC curves cannot be straightforwardly related to those for lower thresholds.
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