Detecting time lag between a pair of time series using visibility graph algorithm

Estimating the time lag between a pair of time series is of significance in many practical applications. In this article, we introduce a method to quantify such lags by adapting the visibility graph algorithm, which converts time series into a mathematical graph. Currently widely used method to dete...

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Vydáno v:Communication in statistics. Case studies and data analysis Ročník 7; číslo 3; s. 315 - 343
Hlavní autoři: John, Majnu, Ferbinteanu, Janina
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Taylor & Francis 2021
Témata:
correlogram
cross correlation
environmental epidemiology
local field potentials
neuroscience
ozone levels
time lag
Time series
transfer function
visibility graph algorithm
transfer function
visibility graph algorithm
cross correlation
correlogram
neuroscience
Time series
ozone levels
time lag
local field potentials
environmental epidemiology
ISSN:2373-7484, 2373-7484
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Abstract Estimating the time lag between a pair of time series is of significance in many practical applications. In this article, we introduce a method to quantify such lags by adapting the visibility graph algorithm, which converts time series into a mathematical graph. Currently widely used method to detect such lags is based on cross-correlations, which has certain limitations. We present simulated examples where the new method identifies the lag correctly and unambiguously while as the cross-correlation method does not. The article includes results from an extensive simulation study conducted to better understand the scenarios where the new method performed better or worse than the existing approach. We also present a likelihood based parametric modeling framework and consider frameworks for quantifying uncertainty and hypothesis testing for the new approach. We apply the current and new methods to two case studies, one from neuroscience and the other from environmental epidemiology, to illustrate the methods further.
AbstractList Estimating the time lag between a pair of time series is of significance in many practical applications. In this article, we introduce a method to quantify such lags by adapting the visibility graph algorithm, which converts time series into a mathematical graph. Currently widely used method to detect such lags is based on cross-correlations, which has certain limitations. We present simulated examples where the new method identifies the lag correctly and unambiguously while as the cross-correlation method does not. The article includes results from an extensive simulation study conducted to better understand the scenarios where the new method performed better or worse than the existing approach. We also present a likelihood based parametric modeling framework and consider frameworks for quantifying uncertainty and hypothesis testing for the new approach. We apply the current and new methods to two case studies, one from neuroscience and the other from environmental epidemiology, to illustrate the methods further.
Estimating the time lag between a pair of time series is of significance in many practical applications. In this article, we introduce a method to quantify such lags by adapting the visibility graph algorithm, which converts time series into a mathematical graph. Currently widely used method to detect such lags is based on cross-correlations, which has certain limitations. We present simulated examples where the new method identifies the lag correctly and unambiguously while as the cross-correlation method does not. The article includes results from an extensive simulation study conducted to better understand the scenarios where the new method performed better or worse than the existing approach. We also present a likelihood based parametric modeling framework and consider frameworks for quantifying uncertainty and hypothesis testing for the new approach. We apply the current and new methods to two case studies, one from neuroscience and the other from environmental epidemiology, to illustrate the methods further.Estimating the time lag between a pair of time series is of significance in many practical applications. In this article, we introduce a method to quantify such lags by adapting the visibility graph algorithm, which converts time series into a mathematical graph. Currently widely used method to detect such lags is based on cross-correlations, which has certain limitations. We present simulated examples where the new method identifies the lag correctly and unambiguously while as the cross-correlation method does not. The article includes results from an extensive simulation study conducted to better understand the scenarios where the new method performed better or worse than the existing approach. We also present a likelihood based parametric modeling framework and consider frameworks for quantifying uncertainty and hypothesis testing for the new approach. We apply the current and new methods to two case studies, one from neuroscience and the other from environmental epidemiology, to illustrate the methods further.
Author John, Majnu
Ferbinteanu, Janina
AuthorAffiliation b Department of Psychiatry, Hofstra University, Hempstead, NY, USA
c The Feinstein Institute of Medical Research, Northwell Health System, Manhasset, NY, USA
f Department of Neurology, SUNY Downstate, Brooklyn, NY, USA
e Department of Physiology and Pharmacology, SUNY Downstate, Brooklyn, NY, USA
d Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA
a Department of Mathematics, Hofstra University, Hempstead, NY, USA
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visibility graph algorithm
cross correlation
correlogram
neuroscience
Time series
ozone levels
time lag
local field potentials
environmental epidemiology
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Snippet Estimating the time lag between a pair of time series is of significance in many practical applications. In this article, we introduce a method to quantify...
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SubjectTerms correlogram
cross correlation
environmental epidemiology
local field potentials
neuroscience
ozone levels
time lag
Time series
transfer function
visibility graph algorithm
Title Detecting time lag between a pair of time series using visibility graph algorithm
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