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Research and application of time series of the causal relationship between Grainger triggered the complex network analysis method based on

Author: CuiDong
Tutor: LinWei
School: Fudan University
Course: Applied Mathematics
Keywords: Granger Causality Complex Network ECG Analysis Autoregressive Model Neural Network Radial Basis Function Polynomial Function Support Vector Machine
CLC: O157.5
Type: Master's thesis
Year: 2011
Downloads: 228
Quote: 0
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Abstract


This paper proposes an approach of integrating the Granger causality and com-plex networks into characterizing time series with noise perturbations in both the time and the frequency domains. Representative measurements describing the physical and topological properties of a weighted and directional complex network are used in the characterization. Strikingly, the proposed approach shows a strong competency in dis-tinguishing typical dynamical behaviors of time series with noise perturbations. Partic-ularly, this approach, when applied to the human electrocardiograms from the MIT-BIH datasets, becomes practically useful in discriminating the normal sinus rhythm subjects from those with arrhythmia symptom. In addition, the research of nonlinear Granger causality has just started, this paper also innovatively integrates artificial neural net-works into Granger causality analysis and builds up the theory framework systemati-cally, designing a new measurement method for nonlinear Granger causality.

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CLC: > Mathematical sciences and chemical > Mathematics > Algebra,number theory, portfolio theory > Combinatorics ( combinatorics ) > Graph Theory
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