Search results for "TIME SERIES"

showing 10 items of 247 documents

Graph recursive least squares filter for topology inference in causal data processes

2017

In this paper, we introduce the concept of recursive least squares graph filters for online topology inference in data networks that are modelled as Causal Graph Processes (CGP). A Causal Graph Process (CGP) is an auto regressive process in the time series associated to different variables, and whose coefficients are the so-called graph filters, which are matrix polynomials with different orders of the graph adjacency matrix. Given the time series of data at different variables, the goal is to estimate these graph filters, hence the associated underlying adjacency matrix. Previously proposed algorithms have focused on a batch approach, assuming implicitly stationarity of the CGP. We propose…

Recursive least squares filterSignal processingMean squared errorComputer science020206 networking & telecommunications02 engineering and technologyCall graphNetwork topology0202 electrical engineering electronic engineering information engineeringGraph (abstract data type)020201 artificial intelligence & image processingAdjacency matrixTime seriesAlgorithm2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
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Synergetic and redundant information flow detected by unnormalized Granger causality: application to resting state fMRI

2015

Objectives: We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. Methods: The presence of redundancy and/or synergy in multivariate time series data renders difficult to estimate the neat flow of information from each driver variable to a given target. We show that adopting an unnormalized definition of Granger causality one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables. Consequently we introduce a pairwise index of synergy which is zero when two in…

FOS: Computer and information sciencesgranger causality (GC)Multivariate statisticsComputer scienceRestComputer Science - Information TheoryBiomedical EngineeringsynergyFOS: Physical sciencescomputer.software_genre01 natural sciences03 medical and health sciences0302 clinical medicineGranger causality0103 physical sciencesConnectomeRedundancy (engineering)HumansBrain connectivityTime series010306 general physicsModels StatisticalHuman Connectome ProjectResting state fMRIredundancybusiness.industryInformation Theory (cs.IT)functional magnetic resonance imaging (fMRI)BrainPattern recognitionComplex networkMagnetic Resonance ImagingVariable (computer science)Physics - Data Analysis Statistics and ProbabilityQuantitative Biology - Neurons and CognitionFOS: Biological sciencesSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaPairwise comparisonNeurons and Cognition (q-bio.NC)Artificial intelligenceData miningNerve Netbusinesscomputer030217 neurology & neurosurgeryData Analysis Statistics and Probability (physics.data-an)
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Modeling foreign exchange market activity around macroeconomic news: Hawkes-process approach

2015

We present a Hawkes-model approach to the foreign exchange market in which the high-frequency price dynamics is affected by a self-exciting mechanism and an exogenous component, generated by the pre-announced arrival of macroeconomic news. By focusing on time windows around the news announcement, we find that the model is able to capture the increase of trading activity after the news, both when the news has a sizable effect on volatility and when this effect is negligible, either because the news in not important or because the announcement is in line with the forecast by analysts. We extend the model by considering noncausal effects, due to the fact that the existence of the news (but not…

news arrivalTime windowsforeign exchange marketHawkes processehigh frequency financeEconomicsMonetary economicsVolatility (finance)Time seriesForeign exchange marketComputingMilieux_MISCELLANEOUS
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Taxonomy of stock market indices

2000

We investigate sets of financial non-redundant and nonsynchronously recorded time series. The sets are composed by a number of stock market indices located all over the world in five continents. By properly selecting the time horizon of returns and by using a reference currency we find a meaningful taxonomy. The detection of such a taxonomy proves that interpretable information can be stored in a set of nonsynchronously recorded time series.

Statistical Finance (q-fin.ST)Statistical Mechanics (cond-mat.stat-mech)Series (mathematics)Computer scienceQuantitative Finance - Statistical FinanceFOS: Physical sciencesTime horizoncomputer.software_genreStock market indexFOS: Economics and businessSet (abstract data type)CurrencyTaxonomy (general)EconometricsData miningTime seriescomputerCondensed Matter - Statistical MechanicsPhysical Review E
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Entropy and Renormalization in Chaotic Visibility Graphs

2016

PhysicsCombinatoricsRenormalizationNonlinear time series analysisGraph entropy0103 physical sciencesChaoticEntropy (information theory)Statistical physics010306 general physics01 natural sciences010305 fluids & plasmas
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Vegetation dynamics from NDVI time series analysis using the wavelet transform

2009

A multi-resolution analysis (MRA) based on the wavelet transform (WT) has been implemented to study NDVI time series. These series, which are non-stationary and present short-term, seasonal and long-term variations, can be decomposed using this MRA as a sum of series associated with different temporal scales. The main focus of the paper is to check the potential of this MRA to capture and describe both intra- and inter-annual changes in the data, i.e., to discuss the ability of the proposed procedure to monitor vegetation dynamics at regional scale. Our approach concentrates on what wavelet analysis can tell us about a NDVI time series. On the one hand, the intra-annual series, linked to th…

Advanced very-high-resolution radiometerSoil ScienceWavelet transformGeologyVegetationLand coverSeasonalitymedicine.diseaseNormalized Difference Vegetation IndexWaveletmedicineComputers in Earth SciencesTime seriesRemote sensingMathematicsRemote Sensing of Environment
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Deep Learning Models Performance For NDVI Time Series Prediction: A Case Study On North West Tunisia

2020

The main goal of this paper is to analyze the performance of two deep learning models Long Short-Term Memory (LSTM) and bidirectional LSTM (BiLSTM) network for non-stationary Normalized Difference Vegetation Index (NDVI) time-series prediction. Both methods have provided good performances in the different time series. The BiLSTM has shown the best agreement with the lowest root mean square error (RMSE) and the highest Pearson correlation coefficient (R) of 0.034 and 0.93, respectively.

Mean squared errorSeries (mathematics)business.industryDeep learningNormalized Difference Vegetation IndexPearson product-moment correlation coefficientsymbols.namesakeNorth westStatisticssymbolsmedicineArtificial intelligenceTime seriesmedicine.symptombusinessVegetation (pathology)Mathematics2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS)
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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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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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Trends in phenological parameters and relationship between land surface phenology and climate data in the Hyrcanian forests of Iran

2017

Vegetation activity may be changed in response to climate variability by affecting seasonality and phenological events. Monitoring of land surface phenological changes play a key role in understanding feedback of ecosystem dynamics. This study focuses on the analysis of trends in land surface phenology derived parameters using normalized difference vegetation index time series based on Global Inventory Monitoring and Mapping Studies data in the Hyrcanian forests of Iran covering the period 1981–2012. First, we applied interpolation for data reconstruction in order to remove outliers and cloud contamination in time series. Phenological parameters were retrieved by using the midpoint approach…

Atmospheric Science010504 meteorology & atmospheric sciencesPhenology0211 other engineering and technologies1903 Computers in Earth Sciences02 engineering and technologyVegetationSeasonalitymedicine.disease01 natural sciencesNormalized Difference Vegetation IndexTrend analysis10122 Institute of GeographyClimatologyLinear regression1902 Atmospheric SciencemedicineEnvironmental sciencePrecipitationTime series910 Geography & travelComputers in Earth Sciences021101 geological & geomatics engineering0105 earth and related environmental sciences
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