Early warnings of unknown nonlinear shifts: a nonparametric approach
Early warnings of unknown nonlinear shifts: a nonparametric approach
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DOI：
10.1890/11-0716.1
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发表时间：
2011-12-01
期刊：
影响因子：
4.8
通讯作者：
Brock, W. A.
中科院分区：
文献类型：
--
作者：
Carpenter, S. R.;Brock, W. A.
在真实的自然数据生成过程不确定的情况下，体制转变的预警信号（EWS）具有挑战性。非参数漂移 - 扩散 - 跳跃模型通过拟合一个能够近似多种数据生成过程的通用模型来解决这一问题。漂移衡量局部变化率。扩散衡量在每个时间步长发生的相对较小的冲击。跳跃是较大的间歇性冲击。总方差结合了扩散和跳跃的影响。非参数方法非常适合用于自动化高频传感器的新兴技术。总方差是测量最精确的指标。跳跃强度似乎是一种有用的预警信号。除非有包含多次体制转变的长时间序列数据，否则漂移的估计具有高度不确定性。由漂移估计值（如自相关系数或回报率）计算出的预警信号精度较低，应谨慎使用。尽管如此，就目前的知识状况而言，忽视任何潜在的预警信号还为时过早。
Early warning signals (EWS) of regime shifts are challenging in cases where the true natural data-generating process is uncertain. Nonparametric drift-diffusion-jump models address this problem by fitting a general model that can approximate a wide range of data-generating processes. Drift measures the local rate of change. Diffusion measures relatively small shocks that occur at each time step. Jumps are large intermittent shocks. Total variance combines the contributions of diffusion and jumps. Nonparametric methods are well suited to emerging technology for automated, high-frequency sensors. Total variance is the most precisely measured indicator. Jump intensity appears to be a useful EWS. Estimates of the drift are highly uncertain unless long time series with many regime shifts are available. EWS computed from drift estimates (such as autocorrelation coefficients or return rates) have low precision and should be used with caution. Nonetheless, in the current state of knowledge, it is premature to disregard any potential EWS.