6533b857fe1ef96bd12b455d
RESEARCH PRODUCT
Multiscale analysis of information dynamics for linear multivariate processes.
Alessandro MontaltoLuca FaesDaniele MarinazzoGiandomenico NolloSebastiano Stramagliasubject
FOS: Computer and information sciencesInformation transferMultivariate statisticsMultivariate analysisComputer scienceComputer Science - Information Theory0206 medical engineeringStochastic ProcesseBiomedical EngineeringFOS: Physical sciencesInformation Storage and RetrievalHealth Informatics02 engineering and technology01 natural sciencesEntropy (classical thermodynamics)Moving average0103 physical sciencesEntropy (information theory)Computer SimulationStatistical physicsEntropy (energy dispersal)Time series010306 general physicsEntropy (arrow of time)Multivariate Analysi1707Stochastic ProcessesEntropy (statistical thermodynamics)Stochastic processInformation Theory (cs.IT)Probability and statisticsModels Theoretical020601 biomedical engineeringComplex dynamicsAutoregressive modelPhysics - Data Analysis Statistics and ProbabilitySignal ProcessingSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaMultivariate AnalysisData Analysis Statistics and Probability (physics.data-an)Entropy (order and disorder)description
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 state-space (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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-01-01 | Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference |