论文标题
内核希尔伯特空间中的明确格兰杰因果关系
Explicit Granger causality in kernel Hilbert spaces
论文作者
论文摘要
Granger因果关系（GC）无疑是从观察时间序列中推断出原因效应关系的最广泛使用的方法。基于内核方法提出了几种非线性GC替代方法。我们通过在希尔伯特空间中明确考虑变量的交叉关系来概括内核Granger因果关系。该框架显示出概括线性和内核GC方法的概括，并且基于Rademacher复杂性具有更严格的性能界限。我们成功地评估了其在标准动力学系统中的性能，并确定了耦合的Rössler系统中的箭头箭头，并被利用以披露厄尔尼诺尼诺 - 南方振荡（ENSO）现象在全球含量上的土壤水分上的脚印。
Granger causality (GC) is undoubtedly the most widely used method to infer cause-effect relations from observational time series. Several nonlinear alternatives to GC have been proposed based on kernel methods. We generalize kernel Granger causality by considering the variables cross-relations explicitly in Hilbert spaces. The framework is shown to generalize the linear and kernel GC methods, and comes with tighter bounds of performance based on Rademacher complexity. We successfully evaluate its performance in standard dynamical systems, as well as to identify the arrow of time in coupled Rössler systems, and is exploited to disclose the El Niño-Southern Oscillation (ENSO) phenomenon footprints on soil moisture globally.
