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Permanent link (DOI): https://doi.org/10.7939/R3Z02ZH70
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Nonlinear Dynamic Causality Inference in Time Series Open Access
- Other title
Gene regulatory network inference
Nonlinear dynamic causality
- Type of item
- 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 of Electrical and Computer Engineering
- Date accepted
- Graduation date
Doctor of Philosophy
- Degree level
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.
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- 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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