Search results for "multivariate time series"

showing 4 items of 14 documents

Information Transfer in Linear Multivariate Processes Assessed through Penalized Regression Techniques: Validation and Application to Physiological N…

2020

The framework of information dynamics allows the dissection of the information processed in a network of multiple interacting dynamical systems into meaningful elements of computation that quantify the information generated in a target system, stored in it, transferred to it from one or more source systems, and modified in a synergistic or redundant way. The concepts of information transfer and modification have been recently formulated in the context of linear parametric modeling of vector stochastic processes, linking them to the notion of Granger causality and providing efficient tools for their computation based on the state&ndash

conditional transfer entropyInformation transferlinear predictionDynamical systems theoryComputer scienceState–space modelsGeneral Physics and Astronomylcsh:AstrophysicsNetwork topologycomputer.software_genrenetwork physiology01 natural sciencesArticle03 medical and health sciences0302 clinical medicinepenalized regression techniquelcsh:QB460-4660103 physical sciencesEntropy (information theory)Statistics::Methodologylcsh:Science010306 general physicspartial information decompositionmultivariate time series analysisinformation dynamics; partial information decomposition; entropy; conditional transfer entropy; network physiology; multivariate time series analysis; State–space models; vector autoregressive model; penalized regression techniques; linear predictionState–space modellcsh:QC1-999multivariate time series analysiInformation dynamicData pointpenalized regression techniquesAutoregressive modelSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaParametric modelOrdinary least squaresvector autoregressive modellcsh:QData mininginformation dynamicsentropycomputerlcsh:Physics030217 neurology & neurosurgery
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Assessing Complexity in Physiological Systems through Biomedical Signals Analysis

2020

The idea that most physiological systems are complex has become increasingly popular in recent decades [...]

information flowComputer sciencebrainMultivariate time series analysisGeneral Physics and Astronomylcsh:AstrophysicsData sciencelcsh:QC1-999Fetal heart rateFuzzy entropyEditorialmultifractalitymultiscaleSettore ING-INF/06 - Bioingegneria Elettronica E Informaticalcsh:QB460-466Autonomic nervous functionBrain; Cardiovascular system; Entropy; Information flow; Multifractality; Multiscalecardiovascular systemHypobaric hypoxialcsh:QInformation dynamicsentropylcsh:Sciencelcsh:PhysicsEntropy
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Information transfer and information modification to identify the structure of cardiovascular and cardiorespiratory networks

2017

To fully elucidate the complex physiological mechanisms underlying the short-term autonomic regulation of heart period (H), systolic and diastolic arterial pressure (S, D) and respiratory (R) variability, the joint dynamics of these variables need to be explored using multivariate time series analysis. This study proposes the utilization of information-theoretic measures to measure causal interactions between nodes of the cardiovascular/cardiorespiratory network and to assess the nature (synergistic or redundant) of these directed interactions. Indexes of information transfer and information modification are extracted from the H, S, D and R series measured from healthy subjects in a resting…

medicine.medical_specialtyInformation transferPosture0206 medical engineeringBiomedical EngineeringBlood PressureHealth Informatics02 engineering and technologycomputer.software_genreCardiovascular SystemDiastolic arterial pressureAutonomic regulation03 medical and health sciences0302 clinical medicineHeart RateInternal medicineBayesian multivariate linear regressionmedicine1707Resting state fMRIbusiness.industryRespirationMultivariate time series analysisHealthy subjectsCardiorespiratory fitness020601 biomedical engineeringSignal ProcessingSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaLinear ModelsCardiologyData miningbusinesscomputer030217 neurology & neurosurgery2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
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ICA and stochastic volatility models

2016

We consider multivariate time series where each component series is an unknown linear combination of latent mutually independent stationary time series. Multivariate financial time series have often periods of low volatility followed by periods of high volatility. This kind of time series have typically non-Gaussian stationary distributions, and therefore standard independent component analysis (ICA) tools such as fastICA can be used to extract independent component series even though they do not utilize any information on temporal dependence. In this paper we review some ICA methods used in the context of stochastic volatility models. We also suggest their modifications which use nonlinear…

nonlinear autocorrelationmultivariate time seriesblind source separationGARCH model
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