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Data-Driven Approach for Inferencing Causality and Network Topology
- Source :
- ACC
- Publication Year :
- 2018
- Publisher :
- IEEE, 2018.
-
Abstract
- In this paper, we provide a novel approach to capture causal interaction in a linear dynamical system from time-series data. In [1], we have shown that the existing measures of information transfer, namely directed information, Granger causality and transfer entropy fail to capture true causal interaction in a dynamical system and proposed a new definition of information transfer that captures true causal interaction. The main contribution of this paper is to show that the proposed definition of information transfer in [1] [2] can be computed from time-series data and the computed information measure allows the identification of causal interaction and network topology in a dynamical system. The data-driven algorithm for computation of information transfer for linear systems relies on a robust optimization formulation of transfer operator theoretic framework. The proposed technique is applied to a number of different examples to establish its efficiency.
- Subjects :
- 0209 industrial biotechnology
Information transfer
Theoretical computer science
Computer science
Entropy (statistical thermodynamics)
Linear system
Robust optimization
02 engineering and technology
Network topology
01 natural sciences
Causality
Linear dynamical system
Entropy (classical thermodynamics)
020901 industrial engineering & automation
Granger causality
Robustness (computer science)
Transfer operator
0103 physical sciences
Entropy (information theory)
Transfer entropy
Entropy (energy dispersal)
Time series
010306 general physics
Entropy (arrow of time)
Entropy (order and disorder)
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2018 Annual American Control Conference (ACC)
- Accession number :
- edsair.doi...........9cb3744a191029ac4ae0baa2af93889b