Search results for " Time series analysis"

showing 10 items of 30 documents

Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators

2021

One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a state-space (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by solving only one regression problem. While this problem is typically solved through Ordinary Least Sq…

Artificial neural networks; Chaotic oscillators; Granger causality; Multivariate time series analysis; Network physiology; Penalized regression techniques; Remote synchronization; State-space models; Stochastic gradient descent L1; Vector autoregressive modelGeneral Computer ScienceDynamical systems theoryComputer science02 engineering and technologyChaotic oscillatorsPenalized regression techniquesNetwork topologySettore ING-INF/01 - ElettronicaMultivariate time series analysisVector autoregression03 medical and health sciences0302 clinical medicineScientific Computing and Simulation0202 electrical engineering electronic engineering information engineeringRepresentation (mathematics)Optimization Theory and ComputationNetwork physiologyState-space modelsArtificial neural networkArtificial neural networksData ScienceTheory and Formal MethodsQA75.5-76.95Stochastic gradient descent L1Granger causality State-space models Vector autoregressive model Artificial neural networks Stochastic gradient descent L1 Multivariate time series analysis Network physiology Remote synchronization Chaotic oscillators Penalized regression techniquesRemote synchronizationStochastic gradient descentAutoregressive modelAlgorithms and Analysis of AlgorithmsVector autoregressive modelElectronic computers. Computer scienceSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causality020201 artificial intelligence & image processingGradient descentAlgorithm030217 neurology & neurosurgeryPeerJ Computer Science
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Methodological considerations for interrupted time series analysis in radiation epidemiology: an overview

2021

Interrupted time series analysis (ITSA) is a method that can be applied to evaluate health outcomes in populations exposed to ionizing radiation following major radiological events. Using aggregated time series data, ITSA evaluates whether the time trend of a health indicator shows a change associated with the radiological event. That is, ITSA checks whether there is a statistically significant discrepancy between the projection of a pre-event trend and the data empirically observed after the event. Conducting ITSA requires one to consider specific methodological issues due to unique threats to internal validity that make ITSA prone to bias. We here discuss the strengths and limitations of …

Computer scienceConfoundingPublic Health Environmental and Occupational HealthInterrupted Time Series AnalysisStatistical modelGeneral MedicineHealth indicatorInterrupted Time Series AnalysisResearch DesignData qualityEconometricsInternal validityTime seriesSpurious relationshipWaste Management and DisposalForecastingJournal of Radiological Protection
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Statistical geometric affinity in human brain electric activity

2007

10 pages, 9 figures.-- PACS nrs.: 87.19.La; 05.45.Tp.-- ISI Article Identifier: 000246890100105

Computer scienceModels NeurologicalNeurophysiologyElectroencephalographyInterpretation (model theory)[PACS] Time series analysis (nonlinear dynamical systems)LacunaritymedicineHumansComputer SimulationDiagnosis Computer-AssistedWakefulnessRepresentation (mathematics)ScalingEvoked PotentialsModels Statisticalmedicine.diagnostic_testbusiness.industry[PACS] Neuroscience (higher organisms)BrainPattern recognitionElectroencephalographyNeurophysiologyAmplitudeStatistical analysisData Interpretation StatisticalBioelectric phenomenaLacunarityAffine transformationArtificial intelligenceSleep StagesbusinessSleep
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Minimally Invasive Assessment of Mental Stress based on Wearable Wireless Physiological Sensors and Multivariate Biosignal Processing

2019

The development of connected health technologies for the continuous monitoring of the psychophysical state of individuals performing daily life activities requires the aggregation of non-intrusive sensors and the availability of methods and algorithms for extracting the relevant physiological information. The present study proposes an integrated approach for the objective assessment of mental stress which combines wirelessly connected low invasive biosensors with multivariate physiological time series analysis. In a group of 18 healthy subjects monitored in a relaxed resting state and during two experimental conditions inducing mental stress and sustained attention (respectively, mental ari…

Computer scienceWearable computerwearable deviceElectroencephalographySettore ING-INF/01 - Elettronica03 medical and health sciences0302 clinical medicinetime series analysimedicineTime domainBiosignalEEGstress assessmentTime series030304 developmental biology0303 health sciencesResting state fMRImedicine.diagnostic_testbusiness.industryContinuous monitoringPattern recognitionphysiological signalConnected healthSettore ING-INF/06 - Bioingegneria Elettronica E Informaticaphysiological signals EEG stress assessment time series analysis wearable devicesArtificial intelligencebusiness030217 neurology & neurosurgery
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Spectral decomposition of cerebrovascular and cardiovascular interactions in patients prone to postural syncope and healthy controls.

2022

We present a framework for the linear parametric analysis of pairwise interactions in bivariate time series in the time and frequency domains, which allows the evaluation of total, causal and instantaneous interactions and connects time- and frequency-domain measures. The framework is applied to physiological time series to investigate the cerebrovascular regulation from the variability of mean cerebral blood flow velocity (CBFV) and mean arterial pressure (MAP), and the cardiovascular regulation from the variability of heart period (HP) and systolic arterial pressure (SAP). We analyze time series acquired at rest and during the early and late phase of head-up tilt in subjects developing or…

Endocrine and Autonomic SystemsTime series analysisBlood PressureHeartBaroreflexCardiovascular SystemSyncopeCerebral autoregulationCellular and Molecular NeuroscienceHeart RateAutoregressive modelsCardiovascular controlCerebrovascular CirculationGranger causalitySettore ING-INF/06 - Bioingegneria Elettronica e InformaticaHumansNeurology (clinical)Spectral decompositionAutoregressive models; Cardiovascular control; Cerebral autoregulation; Granger causality; Spectral decomposition; Time series analysis;Autonomic neuroscience : basicclinical
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On the interpretability and computational reliability of frequency-domain Granger causality

2017

This Correspondence article is a comment which directly relates to the paper “A study of problems encountered in Granger causality analysis from a neuroscience perspective” (Stokes and Purdon, 2017). We agree that interpretation issues of Granger causality (GC) in neuroscience exist, partially due to the historically unfortunate use of the name “causality”, as described in previous literature. On the other hand, we think that Stokes and Purdon use a formulation of GC which is outdated (albeit still used) and do not fully account for the potential of the different frequency-domain versions of GC; in doing so, their paper dismisses GC measures based on a suboptimal use of them. Furthermore, s…

FOS: Computer and information sciences0301 basic medicineTheoretical computer scienceImmunology and Microbiology (all)Computer scienceTime series analysiMathematics - Statistics TheoryStatistics Theory (math.ST)Statistics - ApplicationsGeneral Biochemistry Genetics and Molecular BiologyMethodology (stat.ME)Causality (physics)03 medical and health sciences0302 clinical medicinegranger causalityGranger causalityCorrespondenceFOS: MathematicsApplications (stat.AP)Physiological oscillationGeneral Pharmacology Toxicology and PharmaceuticsTime seriessignal processingStatistical Methodologies & Health Informaticsfrequency-domain connectivityReliability (statistics)Statistics - MethodologyInterpretabilityGranger-Geweke causalityBiochemistry Genetics and Molecular Biology (all)Interpretation (logic)General Immunology and Microbiologybrain connectivityGeneral MedicineArticlesvector autoregressive models030104 developmental biologyMathematics and StatisticsWildcardVector autoregressive modelPharmacology Toxicology and Pharmaceutics (all)Frequency domaintime series analysisspectral decompositionSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaBrain connectivity; Directed coherence; Frequency-domain connectivity; Granger-Geweke causality; Physiological oscillations; Spectral decomposition; Time series analysis; Vector autoregressive models; Biochemistry Genetics and Molecular Biology (all); Immunology and Microbiology (all); Pharmacology Toxicology and Pharmaceutics (all)directed coherence030217 neurology & neurosurgeryphysiological oscillations
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Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes

2017

Exploiting the theory of state space models, we derive the exact expressions of the information transfer, as well as redundant and synergistic transfer, for coupled Gaussian processes observed at multiple temporal scales. All of the terms, constituting the frameworks known as interaction information decomposition and partial information decomposition, can thus be analytically obtained for different time scales from the parameters of the VAR model that fits the processes. We report the application of the proposed methodology firstly to benchmark Gaussian systems, showing that this class of systems may generate patterns of information decomposition characterized by prevalently redundant or sy…

FOS: Computer and information sciencesInformation transferComputer scienceGaussianSocial SciencesGeneral Physics and AstronomyInformation theory01 natural sciences010305 fluids & plasmasState spaceStatistical physicslcsh:Scienceinformation theorymultiscale entropylcsh:QC1-999Interaction informationMathematics and Statisticssymbolsinformation dynamicsInformation dynamics; Information transfer; Multiscale entropy; Multivariate time series analysis; Redundancy and synergy; State space models; Vector autoregressive models; Physics and Astronomy (all)information dynamics; information transfer; multiscale entropy; multivariate time series analysis; redundancy and synergy; state space models; vector autoregressive modelsMultivariate time series analysiMathematics - Statistics Theorylcsh:AstrophysicsStatistics Theory (math.ST)Statistics - ApplicationsMethodology (stat.ME)symbols.namesakePhysics and Astronomy (all)0103 physical scienceslcsh:QB460-466FOS: Mathematicsinformation transferRelevance (information retrieval)Applications (stat.AP)Transfer Entropy010306 general physicsGaussian processStatistics - MethodologyState space modelstate space modelsmultivariate time series analysisredundancy and synergyvector autoregressive modelsInformation dynamicVector autoregressive modelSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaTransfer entropylcsh:Qlcsh:PhysicsEntropy
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Using the Scaling Analysis to Characterize Financial Markets

2003

We empirically analyze the scaling properties of daily Foreign Exchange rates, Stock Market indices and Bond futures across different financial markets. We study the scaling behaviour of the time series by using a generalized Hurst exponent approach. We verify the robustness of this approach and we compare the results with the scaling properties in the frequency-domain. We find evidence of deviations from the pure Brownian motion behavior. We show that these deviations are associated with characteristics of the specific markets and they can be, therefore, used to distinguish the different degrees of development of the markets.

FOS: Economics and businessStatistical Finance (q-fin.ST)Statistical Mechanics (cond-mat.stat-mech)jel:G1Quantitative Finance - Statistical FinanceFOS: Physical sciencesCondensed Matter - Statistical Mechanicsscaling exponents time series analysis multi-fractals financial market
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Investigating effects in GNSS station coordinate time series

2014

The vertical and horizontal displacements of the Earth can be measured to a high degree of precision using GNSS. Time series of Latvian GNSS station positions of both the EUPOS®-Riga and LatPos networks have been developed at the Institute of Geodesy and Geoinformation of the University of Latvia (LU GGI). In this study the main focus is made on the noise analysis of the obtained time series and site displacement identification. The results of time series have been analysed and distinctive behaviour of EUPOS®-Riga and LatPos station coordinate changes have been identified. The possible dependences of GNSS station coordinate distribution on EPN station problems, seismic activity of some area…

Geographic information systemHorizontal and verticalSeries (mathematics)business.industryGNSS permanent networks time series analysis station displacementsCoordinate timeGeodesyDisplacement (vector)NoiseGeographyGNSS applicationsTime seriesbusinessRemote sensingProceedings of the International Conference „Innovative Materials, Structures and Technologies”
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Information Decomposition in Multivariate Systems: Definitions, Implementation and Application to Cardiovascular Networks

2016

The continuously growing framework of information dynamics encompasses a set of tools, rooted in information theory and statistical physics, which allow to quantify different aspects of the statistical structure of multivariate processes reflecting the temporal dynamics of complex networks. Building on the most recent developments in this field, this work designs a complete approach to dissect the information carried by the target of a network of multiple interacting systems into the new information produced by the system, the information stored in the system, and the information transferred to it from the other systems; information storage and transfer are then further decomposed into amou…

Information transferDynamical systems theoryComputer scienceGeneral Physics and Astronomylcsh:AstrophysicsInformation theorycomputer.software_genreMachine learning01 natural sciencesEntropy - Cardiorespiratory interactions - Dynamical systems -cardiovascular interactions03 medical and health sciencessymbols.namesake0302 clinical medicinelcsh:QB460-4660103 physical sciencesinformation transferEntropy (information theory)lcsh:Science010306 general physicsGaussian processautoregressive processesmultivariate time series analysisbusiness.industryautonomic nervous systemredundancy and synergycardiorespiratory interactionsdynamical systemsComplex networkNetwork dynamicslcsh:QC1-999autonomic nervous system; autoregressive processes; cardiorespiratory interactions; cardiovascular interactions; Granger causality; dynamical systems; information dynamics; information transfer; redundancy and synergy; multivariate time series analysisAutoregressive modelSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causalitysymbolslcsh:QArtificial intelligenceData mininginformation dynamicsbusinesscomputerlcsh:Physics030217 neurology & neurosurgeryEntropy; Volume 19; Issue 1; Pages: 5
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