Multiscale analysis of information dynamics for linear multivariate processes

Luca Faes, Daniele Marinazzo, Luca Faes, Alessandro Montalto, Sebastiano Stramaglia, Giandomenico Nollo

Risultato della ricerca: Otherpeer review

4 Citazioni (Scopus)

Abstract

In the study of complex physical and physiological systems represented by multivariate time series, an issue of great interest is the description of the system dynamics over a range of different temporal scales. While information-theoretic approaches to the multiscale analysis of complex dynamics are being increasingly used, the theoretical properties of the applied measures are poorly understood. This study introduces for the first time a framework for the analytical computation of information dynamics for linear multivariate stochastic processes explored at different time scales. After showing that the multiscale processing of a vector autoregressive (VAR) process introduces a moving average (MA) component, we describe how to represent the resulting VARMA process using statespace (SS) models and how to exploit the SS model parameters to compute analytical measures of information storage and information transfer for the original and rescaled processes. The framework is then used to quantify multiscale information dynamics for simulated unidirectionally and bidirectionally coupled VAR processes, showing that rescaling may lead to insightful patterns of information storage and transfer but also to potentially misleading behaviors.
Lingua originaleEnglish
Pagine5489-5492
Numero di pagine4
Stato di pubblicazionePublished - 2016

All Science Journal Classification (ASJC) codes

  • ???subjectarea.asjc.1700.1711???
  • ???subjectarea.asjc.2200.2204???
  • ???subjectarea.asjc.1700.1707???
  • ???subjectarea.asjc.2700.2718???

Fingerprint

Entra nei temi di ricerca di 'Multiscale analysis of information dynamics for linear multivariate processes'. Insieme formano una fingerprint unica.

Cita questo