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    Spectral Approach to Modeling Dependence in Multivariate Time Series

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    _Ombao_2019_J._Phys.__Conf._Ser._1417_012007.pdf
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    Type
    Conference Paper
    Authors
    Ombao, Hernando cc
    KAUST Department
    Biostatistics Group
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    Date
    2019-12-20
    Permanent link to this record
    http://hdl.handle.net/10754/662160
    
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    Abstract
    Consider a multivariate time series such as prices of stocks from various sectors, amount of rainfall in many geographical locations, and brain signals from many different locations on the scalp. The goal of this paper is to present the spectral approach to modeling dependence between components of the multivariate time series. There are many measures of dependence-the most popular being cross-correlation or partial cross-correlation. This measure is easy to compute and easy to understand but it coarse in a sense that it is not able to identify the underlying frequencies that are responsible for driving the dependence. In the stock price example, two stocks may be highly correlated but it would be helpful to see if this correlation is driven by the daily fluctuations or by millisecond-level fluctuations. In the neuroscience example, when two brain regions exhibit a high level of correlation, it will be important if this synchronicity is due to low-frequency oscillations or high-frequency oscillations. Here we present an overview of the underlying principles through specific spectral models which decompose the signals into oscillations of various frequencies and then model lead-lag dependence via these oscillations.
    Citation
    Ombao, H. (2019). Spectral Approach to Modeling Dependence in Multivariate Time Series. Journal of Physics: Conference Series, 1417, 012007. doi:10.1088/1742-6596/1417/1/012007
    Publisher
    IOP Publishing
    Conference/Event name
    Mathematics, Informatics, Science and Education International Conference 2019, MISEIC 2019
    DOI
    10.1088/1742-6596/1417/1/012007
    Additional Links
    https://iopscience.iop.org/article/10.1088/1742-6596/1417/1/012007
    ae974a485f413a2113503eed53cd6c53
    10.1088/1742-6596/1417/1/012007
    Scopus Count
    Collections
    Conference Papers; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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