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.

Faes, L., Montalto, A., Stramaglia, S., Nollo, G., Marinazzo, D. (2016). Multiscale analysis of information dynamics for linear multivariate processes. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS) (pp.5489-5492). Institute of Electrical and Electronics Engineers Inc. [10.1109/EMBC.2016.7591969].

Multiscale analysis of information dynamics for linear multivariate processes

Faes, Luca;
2016-01-01

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.
ago-2016
38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Orlando; United States
16-20 August 2016
2016
2016
4
Online
https://ieeexplore.ieee.org/document/7591969
Faes, L., Montalto, A., Stramaglia, S., Nollo, G., Marinazzo, D. (2016). Multiscale analysis of information dynamics for linear multivariate processes. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS) (pp.5489-5492). Institute of Electrical and Electronics Engineers Inc. [10.1109/EMBC.2016.7591969].
Proceedings (atti dei congressi)
Faes, Luca; Montalto, Alessandro; Stramaglia, Sebastiano; Nollo, Giandomenico; Marinazzo, Daniele
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/273060
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