论文标题
Jacobian Granger因果神经网络，用于分析固定和非组织数据
Jacobian Granger Causal Neural Networks for Analysis of Stationary and Nonstationary Data
论文作者
论文摘要
Granger因果关系是一种常用的方法，用于发现时间序列中的信息流和依赖关系。在这里，我们介绍了JGC（Jacobian Granger因果关系），这是一种基于神经网络的方法，用于使用Jacobian作为可变重要性的量度，并提出了使用此度量来推导Granger因果变量的阈值程序。与识别Granger因果变量，相关时间滞后以及相互作用符号的其他方法相比，所得方法的表现始终如一。最后，通过包含时间变量，我们表明这种方法能够学习granger因果结构在时间上变化的非组织系统的时间依赖性。
Granger causality is a commonly used method for uncovering information flow and dependencies in a time series. Here we introduce JGC (Jacobian Granger Causality), a neural network-based approach to Granger causality using the Jacobian as a measure of variable importance, and propose a thresholding procedure for inferring Granger causal variables using this measure. The resulting approach performs consistently well compared to other approaches in identifying Granger causal variables, the associated time lags, as well as interaction signs. Lastly, through the inclusion of a time variable, we show that this approach is able to learn the temporal dependencies for nonstationary systems whose Granger causal structures change in time.
