Using machine learning to assess short term causal dependence and infer network links
Using machine learning to assess short term causal dependence and infer network links
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DOI：
10.1063/1.5134845
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发表时间：
2019-12-01
期刊：
影响因子：
2.9
通讯作者：
Ott, Edward
中科院分区：
文献类型：
--
作者：
Banerjee, Amitava;Pathak, Jaideep;Ott, Edward
我们介绍并测试了一个通用的基于机器学习的技术，用于从其状态变量的时间序列测量中推断未知动态系统的状态变量之间的短期因果关系。我们的技术利用机器学习过程的结果进行短时间预测，以实现我们的目标。其基本思想是利用机器学习来估计沿着轨道的动力流的雅可比矩阵的元素。我们采用的机器学习类型是水库计算。我们提出了一个相互作用的动态节点的网络链接推理的数值测试。可以看出，动态噪声可以大大提高我们的技术的有效性，而观测噪声降低的有效性。我们相信，这两种相反类型的噪音之间的竞争将是决定因果推理在许多最重要的应用情况下成功的关键因素。由AIP Publishing授权出版。
We introduce and test a general machine-learning-based technique for the inference of short term causal dependence between state variables of an unknown dynamical system from time-series measurements of its state variables. Our technique leverages the results of a machine learning process for short time prediction to achieve our goal. The basic idea is to use the machine learning to estimate the elements of the Jacobian matrix of the dynamical flow along an orbit. The type of machine learning that we employ is reservoir computing. We present numerical tests on link inference of a network of interacting dynamical nodes. It is seen that dynamical noise can greatly enhance the effectiveness of our technique, while observational noise degrades the effectiveness. We believe that the competition between these two opposing types of noise will be the key factor determining the success of causal inference in many of the most important application situations. Published under license by AIP Publishing.