Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Links

Abstract

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.

arXiv comment28 pages, 4 Figures, Accepted in Chaos

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Cited by in corpus (11)

• Parallel Machine Learning for Forecasting the Dynamics of Complex Networks
• Multifunctionality in a Reservoir Computer
• Optimized ensemble deep learning framework for scalable forecasting of dynamics containing extreme events
• Time Shifts to Reduce the Size of Reservoir Computers
• Machine Learning Link Inference of Noisy Delay-coupled Networks with Opto-Electronic Experimental Tests
• Role of assortativity in predicting burst synchronization using echo state network
• Impact of network characteristics on network reconstruction
• Estimating the master stability function from the time series of one oscillator via reservoir computing
• Backpropagation on Dynamical Networks
• Reconstructing Network Structures from Partial Measurements
• Iterative procedure for network inference