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Nonlinear Dynamic Causality Inference in Time Series Open Access

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Other title
Subject/Keyword
Gene regulatory network inference
Distributional causality
Nonlinear dynamic causality
Causality
Causality inference
Financial causality
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Alizad Rahvar, Amir Reza
Supervisor and department
Ardakani, Masoud (Department of Electrical and Computer Engineering)
Examining committee member and department
Cribben, Ivor (Department of Finance and Statistical Analysis, Alberta School of Business)
Chen, Tongwen (Department of Electrical and Computer Engineering)
Khabbazian, Majid (Department of Electrical and Computer Engineering)
Mao, Yongyi (School of Electrical Engineering and Computer Science, University of Ottawa)
Department
Department of Electrical and Computer Engineering
Specialization
Communications
Date accepted
2014-01-23T09:12:03Z
Graduation date
2014-06
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
The main focus of this work is on detection of causal relationships or couplings between different processes or systems. Identification of these causal relationships has applications in many disciplines including physics, economics, biology, neuroscience, and climatology. As these couplings or causal relationships are inherently hidden in the underlying dynamics of the system and are not necessarily accessible, we develop methods to discover these interactions by some observations of the system measured in the form of a time series. In the first part of our work, we propose a new method called the coupling spectrum (CS) for inference of the directed coupling in a deterministic system. We will observe that this method can identify the direction of coupling in sever conditions such as bidirectional couplings, nonlinear dynamics, nonidentical and multivariate systems, small sample sizes, weak couplings, as well as multi-scale and noisy data. Later, we study a biological and a financial application of the CS method. First, we analyze the microarray data for inference of the gene regulatory networks, one of the most important biological networks that their identification has immediate applications in cancer prediction. Then, the CS method is used for detection of the temporal causality between the stock prices of two companies. The analysis of empirical data in these applications show the successful performance of the CS method in real-world problems. In the last part of our contributions, we propose a new method for inference of the distributional causality, a kind of causality that its inference has applications in finance and econometrics. Our method provides information about the influence of the causality on the underlying distribution of the processes. The analysis of the simulated and empirical financial data shows the success of our method.
Language
English
DOI
doi:10.7939/R3Z02ZH70
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
A. R. Alizad-Rahvar, M. Ardakani, “Finding weak directional coupling in multi-scale time Series,” Physical Review E, vol. 86, 2012.A. R. Alizad-Rahvar, M. Ardakani, I. Cribben, “A new method for detecting non-linear causality in time series,” Complex Data Modeling and Computationally Intensive Statistical Methods for estimation and Prediction, Milan, Italy, Sep. 2013.A. R. Alizad-Rahvar, M. Ardakani, I. Cribben, “The Coupling Spectrum: A new method for detecting temporal non-linear causality in financial time series,” the 7th International Days of Statistics and Economics, Prague, Czech, Sep. 2013.

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