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Multiscale granger causality

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Abstract
In the study of complex physical and biological systems represented by multivariate stochastic processes, an issue of great relevance is the description of the system dynamics spanning multiple temporal scales. While methods to assess the dynamic complexity of individual processes at different time scales are well established, multiscale analysis of directed interactions has never been formalized theoretically, and empirical evaluations are complicated by practical issues such as filtering and downsampling. Here we extend the very popular measure of Granger causality (GC), a prominent tool for assessing directed lagged interactions between joint processes, to quantify information transfer across multiple time scales. We show that the multiscale processing of a vector autoregressive (AR) process introduces a moving average (MA) component, and describe how to represent the resulting ARMA process using state space (SS) models and to combine the SS model parameters for computing exact GC values at arbitrarily large time scales. We exploit the theoretical formulation to identify peculiar features of multiscale GC in basic AR processes, and demonstrate with numerical simulations the much larger estimation accuracy of the SS approach compared to pure AR modeling of filtered and downsampled data. The improved computational reliability is exploited to disclose meaningful multiscale patterns of information transfer between global temperature and carbon dioxide concentration time series, both in paleoclimate and in recent years.
Keywords
granger causality, signal processing, temperature, carbon dioxyde, climate

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Citation

Please use this url to cite or link to this publication:

Chicago
Faes, Luca, Giandomenico Nollo, Sebastiano Stramaglia, and Daniele Marinazzo. 2017. “Multiscale Granger Causality.” Physical Review E 96 (4).
APA
Faes, L., Nollo, G., Stramaglia, S., & Marinazzo, D. (2017). Multiscale granger causality. PHYSICAL REVIEW E , 96(4).
Vancouver
1.
Faes L, Nollo G, Stramaglia S, Marinazzo D. Multiscale granger causality. PHYSICAL REVIEW E . American Physical Society (APS); 2017;96(4).
MLA
Faes, Luca, Giandomenico Nollo, Sebastiano Stramaglia, et al. “Multiscale Granger Causality.” PHYSICAL REVIEW E 96.4 (2017): n. pag. Print.
@article{8535592,
  abstract     = {In the study of complex physical and biological systems represented by multivariate stochastic processes, an issue of great relevance is the description of the system dynamics spanning multiple temporal scales. While methods to assess the dynamic complexity of individual processes at different time scales are well established, multiscale analysis of directed interactions has never been formalized theoretically, and empirical evaluations are complicated by practical issues such as filtering and downsampling. Here we extend the very popular measure of Granger causality (GC), a prominent tool for assessing directed lagged interactions between joint processes, to quantify information transfer across multiple time scales. We show that the multiscale processing of a vector autoregressive (AR) process introduces a moving average (MA) component, and describe how to represent the resulting ARMA process using state space (SS) models and to combine the SS model parameters for computing exact GC values at arbitrarily large time scales. We exploit the theoretical formulation to identify peculiar features of multiscale GC in basic AR processes, and demonstrate with numerical simulations the much larger estimation accuracy of the SS approach compared to pure AR modeling of filtered and downsampled data. The improved computational reliability is exploited to disclose meaningful multiscale patterns of information transfer between global temperature and carbon dioxide concentration time series, both in paleoclimate and in recent years.},
  articleno    = {042150},
  author       = {Faes, Luca and Nollo, Giandomenico and Stramaglia, Sebastiano and Marinazzo, Daniele},
  issn         = {2470-0045 },
  journal      = {PHYSICAL REVIEW E },
  keyword      = {granger causality,signal processing,temperature,carbon dioxyde,climate},
  language     = {eng},
  number       = {4},
  publisher    = {American Physical Society (APS)},
  title        = {Multiscale granger causality},
  url          = {http://dx.doi.org/10.1103/physreve.96.042150},
  volume       = {96},
  year         = {2017},
}

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