Information flow between stock markets: A Koopman decomposition approach

Semba, Sherehe; Huiyun, Wan; Gu, Changgui; Yang, Huijie

doi:10.1088/1674-1056/ac16c9

Cited by 4 publications

(2 citation statements)

References 43 publications

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“…Hence, a simple method to extract the patterns with more information preservation in highdimensional gene expressions is still an open challenge. Recently, we have witnessed a rapid development of the Dynamic Mode Decomposition (DMD) [37,38]. It is a pure data-driven mode decomposition technique used to identify the low-dimension coherent structures of complex dynamical systems.…”

Section: Introductionmentioning

confidence: 99%

“…It can not only provides us with the underlying dynamical mechanisms [38], but also be helpful in the prediction and control of complex systems [39,40]. The key idea is that, though a dynamical process is generally nonlinear and complex, when the time duration is short enough it is reasonable to assume the dynamical law underlying the output data keep unchanged [37]. Also, when the time step is small enough the successive later state can be approximated by a linear transformation of the present state.…”

Section: Introductionmentioning

confidence: 99%

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Dynamical patterns in gene expression profile after Influenza A virus (IAV) injection: A dynamic mode decomposition approach

semba

Yang

2022

Preprint

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Background: The records of the interesting genes expressions have stimulated new development of analysis techniques and subsequently understanding the gene co-expressions. However, a simple method to extract the patterns with more preserved information in high-dimensional gene expressions profile is still an open challenge. In this article, the coherent dynamical pattern corresponds to viral recognition receptors were unveiled by employing the Dynamic Mode Decomposition (DMD) to the time-course gene expression profiles. Dynamic mode decomposition executes a lowdimensional spectral decomposition of the records into modes. Results: Each element within a single mode provides a measure of gene’s expression participated in the mode and the gene’s expression phase of oscillation relative to others. The amplitudes quantify modes influence; the associated eigenvalues characterizes the frequency and growth rate of the modes. Using the eigenvalues, dynamic modes and amplitudes, the symptomatic influenza infection individuals are distinguished from the asymptomatic individuals. The symptomatic individuals has one positive real eigenvalue ( βI > 0 ), shows the increase of the receptor response due to the replication of virus. Then, generating exacerbated local immune responses, which results in acute infection and increases pathogenesis. The asymptomatic individuals has two real positive eigenvalues ( βH > 0 )Conclusion: The positive real eigenvalue from the symptomatic individuals shows the increase of the receptor response due to the replication of virus. Then, generating exacerbated local immune responses, which results in acute infection and increases pathogenesis. While the positive eigenvalues from asymptomatic individuals corresponds to the receptors that activates the innate immunity response promoting the viral clearance. Deep understanding of these patterns does not only shed new light on developing the immuno-therapy of influenza A virus but also on emerging acute respiratory distress like a currently novel corona-virus distress-19

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamical patterns in gene expression profile after Influenza A virus (IAV) injection: A dynamic mode decomposition approach

semba

Yang

2022

Preprint

Self Cite

View full text Add to dashboard Cite

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Information flow between BRVM and ESG stock returns: A frequency-dependent analysis

Kyei,

Ampong,

Owusu Junior

et al. 2024

Research in Globalization

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Estimation of Carleman operator from a univariate time series

Semba,

Yang,

Chen

et al. 2024

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Reconstructing a nonlinear dynamical system from empirical time series is a fundamental task in data-driven analysis. One of the main challenges is the existence of hidden variables; we only have records for some variables, and those for hidden variables are unavailable. In this work, the techniques for Carleman linearization, phase-space embedding, and dynamic mode decomposition are integrated to rebuild an optimal dynamical system from time series for one specific variable. Using the Takens theorem, the embedding dimension is determined, which is adopted as the dynamical system’s dimension. The Carleman linearization is then used to transform this finite nonlinear system into an infinite linear system, which is further truncated into a finite linear system using the dynamic mode decomposition technique. We illustrate the performance of this integrated technique using data generated by the well-known Lorenz model, the Duffing oscillator, and empirical records of electrocardiogram, electroencephalogram, and measles outbreaks. The results show that this solution accurately estimates the operators of the nonlinear dynamical systems. This work provides a new data-driven method to estimate the Carleman operator of nonlinear dynamical systems.

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Information flow between stock markets: A Koopman decomposition approach

Cited by 4 publications

References 43 publications

Dynamical patterns in gene expression profile after Influenza A virus (IAV) injection: A dynamic mode decomposition approach

Dynamical patterns in gene expression profile after Influenza A virus (IAV) injection: A dynamic mode decomposition approach

Information flow between BRVM and ESG stock returns: A frequency-dependent analysis

Estimation of Carleman operator from a univariate time series

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