Detection of coupling delay: A problem not yet solved

Coufal, David; Jakubík, Jozef; Jajcay, Nikola; Hlinka, Jaroslav; Krakovská, Anna; Paluš, Milan

doi:10.1063/1.4997757

Cited by 29 publications

(21 citation statements)

References 34 publications

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“…As pointed out in the introduction, this is a potentially important application of transfer entropy, especially in neuroscience [1,16]. Since it is still actively debated whether or not this method is reliable [24], it is sensible to perform numerical tests on well-controlled dynamical systems, even as simple as the present one.…”

Section: Delay Detectionmentioning

confidence: 94%

Influence of time delay on information exchanges between coupled linear stochastic systems

2018

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Time lags are ubiquitous in biophysiological processes and more generally in real-world complex networks. It has been recently proposed to use information-theoretic tools such as transfer entropy to detect and estimate a possible delay in the couplings. In this work, we focus on stationary linear stochastic processes in continuous time and compute the transfer entropy in the presence of delay and correlated noises, using an approximate but numerically effective solution to the relevant Wiener-Hopf factorization problem. Our results rectify and complete the recent study of [1].

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Section: Delay Detectionmentioning

confidence: 94%

Influence of time delay on information exchanges between coupled linear stochastic systems

2018

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“…In contrast, ρ causal ccm ≤ 0 implied that there was no detectable causal effect. We chose this conservative approach to limit the likelihood of false positives; we did not infer strict causal delays, which might be biased [44], but limited our interpretation to causal directionality. Our lagged causality test (Fig.…”

Section: Convergent Cross-mapping (Ccm) Analysesmentioning

confidence: 99%

Forcing of late Pleistocene ice volume by spatially variable insolation

Haaga¹,

Brendryen²,

Diego³

et al. 2018

Preprint

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Changes in Earth's orbit have been dubbed a pacemaker of Quaternary glacial-interglacial climate variability. However, the significance of latitudinally varying insolation as a dynamical forcing of late Pleistocene climate changes remains unclear. Here we use a model-free statespace reconstruction method to quantify the strength of the dynamical influence of locally varying summer energy on global ice volume, with orbitally independent age assignments. Our empirical approach suggests that integrated summer insolation at specific latitudes was a significant driver of ice volume during the past 800,000 years. Summer energy impact on ice volume is detected in a continuous latitudinal band at~50-90 • N, consistent with the role of summer melting of Northern Hemisphere ice sheets predicted by Milankovitch theory. Insolation forcing at southern mid-latitudes strongly covaries with the canonical Milankovitch forcing, and coincides with the subtropical front and the mid-latitude westerlies, the modulation of which has been implicated in Quaternary climate changes. In contrast, the dynamics of summer energy forcing in the Northern Hemisphere south of the extent of ice sheets is different, possibly capturing ice volume sensitivity to latitudinal insolation gradients. Our results show that the importance of external forcing on late Pleistocene ice ages cannot be fully accounted for by a unique insolation forcing time series. The global ice volume response to spatially variable summer energy encompasses a range of physical processes that operate at different times of the year, including forcing signals with a wide spectrum of obliquity-to-precession frequency ratios.

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“…CCM [5,14] is a method for detecting causality between two systems represented by time series. CCM is based on Takens' embedding theorem, exploiting the geometry of attractors of coupled dynamical systems and constructs a map between mutual neighborhoods in state spaces of the coupled dynamical systems under study.…”

Section: Convergent Cross Mappingmentioning

confidence: 99%

“…This runs counter to Granger's intuitive scheme. For more details on the CCM method see [5,14,16,17].…”

Section: Convergent Cross Mappingmentioning

confidence: 99%

An algorithm based on the convergent cross mapping method for the detection of causality in uni-directionally connected chaotic systems

Pukėnas¹

2018

Math. models, eng.

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In this paper, we present some improvements to the convergent cross mapping (CCM) algorithm for detecting causality in uni-directionally connected chaotic systems. The basic concept of the CCM algorithm is that the causal influence of system on system appears as mapping of the neighbouring states in the reconstructed-dimensional manifold, , to the neighbouring states in the reconstructed-dimensional manifold, , and this effect is evaluated using the correlation coefficient between the estimated and observed values of. We proposed a composite indicator of causality as the ratio between the correlation coefficient and the Shannon entropy of the distribution of the residuals between the estimated and observed values of. Application of the proposed approach to four master-slave Rössler and Lorenz systems and real-world data showed that the new algorithm allowed a slight increase in capability to reveal the presence and direction of couplings.

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Detection of coupling delay: A problem not yet solved

Cited by 29 publications

References 34 publications

Influence of time delay on information exchanges between coupled linear stochastic systems

Influence of time delay on information exchanges between coupled linear stochastic systems

Forcing of late Pleistocene ice volume by spatially variable insolation

An algorithm based on the convergent cross mapping method for the detection of causality in uni-directionally connected chaotic systems

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