基于自回归滑动平均过程的统计预警指标
Statistical early-warning indicators based on Auto-Regressive Moving-Average processes
摘要：
我们解决了定义关键转型预警指标的问题。为此，我们通过一类线性模型拟合相关的时间序列，称为自回归移动平均（ARMA（p，q））模型。我们定义了两个指标，代表总的顺序和总的持久性的过程中，链接，分别到的形状和过程的自相关函数的特征衰减时间。我们成功地测试的方法来检测在Langevin模型和2D伊辛模型与最近邻相互作用的转变。然后，我们将该方法应用到复杂系统，即发电机阈值和金融危机检测。
英文摘要：
We address the problem of defining early warning indicators of critical transition. To this purpose, we fit the relevant time series through a class of linear models, known as Auto-Regressive Moving-Average (ARMA(p,q)) models. We define two indicators representing the total order and the total persistence of the process, linked, respectively, to the shape and to the characteristic decay time of the autocorrelation function of the process. We successfully test the method to detect transitions in a Langevin model and a 2D Ising model with nearest-neighbour interaction. We then apply the method to complex systems, namely for dynamo thresholds and financial crisis detection.
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