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JUST: MATLAB and python software for change detection and time series analysis / Ebrahim Ghaderpour in GPS solutions, vol 25 n° 3 (July 2021)  

Public [article]  inGPS solutions > vol 25 n° 3 (July 2021) . - Article 85 Titre : JUST: MATLAB and python software for change detection and time series analysis Type de document : Article/Communication Auteurs : Ebrahim Ghaderpour, Auteur Année de publication : 2021 Article en page(s) : Article 85 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] analyse de données [Termes IGN] détection de changement [Termes IGN] Matlab [Termes IGN] méthode des moindres carrés [Termes IGN] Python (langage de programmation) [Termes IGN] série temporelle Résumé : (Auteur) Change detection within unequally spaced and non-stationary time series is crucial in various applications, such as environmental monitoring and satellite navigation. The jumps upon spectrum and trend (JUST) is developed to detect potential jumps within the trend component of time series segments. JUST can simultaneously estimate the trend and seasonal components of any equally or unequally spaced time series by considering the observational uncertainties or measurement errors. JUST and its modules can also be applied to monitor vegetation time series in near-real-time. Herein, the details of the open-source software package for JUST, developed in both MATLAB and Python, are presented. Numéro de notice : A2021-330 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-021-01118-x date de publication en ligne : 09/04/2021 En ligne : https://doi.org/10.1007/s10291-021-01118-x Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97500 [article]

A new segmentation method for the homogenisation of GNSS-derived IWV time-series / Annarosa Quarello in Journal of the Royal Statistical Society: Series C Applied Statistics, vol inconnu ([01/01/2021])  

Public [article]  inJournal of the Royal Statistical Society: Series C Applied Statistics > vol inconnu [01/01/2021] Titre : A new segmentation method for the homogenisation of GNSS-derived IWV time-series Type de document : Article/Communication Auteurs : Annarosa Quarello , Auteur ; Olivier Bock , Auteur ; Emilie Lebarbier, Auteur Année de publication : 2021 Projets : VEGAN / Bock, Olivier Note générale : bibliographie https://arxiv.org/pdf/2005.04683.pdf Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] coordonnées GNSS [Termes IGN] erreur systématique [Termes IGN] programmation dynamique [Termes IGN] R (langage) [Termes IGN] segmentation [Termes IGN] série temporelle [Termes IGN] station permanente [Termes IGN] teneur intégrée en vapeur d'eau [Termes IGN] variance [Termes IGN] variation saisonnière Résumé : (auteur) Homogenization is an important and crucial step to improve the usage of observational data for climate analysis. This work is motivated by the analysis of long series of GNSS Integrated Water Vapour (IWV) data which have not yet been used in this context. This paper proposes a novel segmentation method that integrates a periodic bias and a heterogeneous, monthly varying, variance. The method consists in estimating first the variance using a robust estimator and then estimating the segmentation and periodic bias iteratively. This strategy allows for the use of the dynamic programming algorithm that remains the most efficient exact algorithm to estimate the change-point positions. The statistical performance of the method is assessed through numerical experiments. An application to a real data set of 120 global GNSS stations is presented. The method is implemented in the R package GNSSseg that will be available on the CRAN. Numéro de notice : A2021-061 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Autre URL associée : vers HAL Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : https://arxiv.org/pdf/2005.04683.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96617 [article]

Self-tuning robust adjustment within multivariate regression time series models with vector-autoregressive random errors / Boris Kargoll in Journal of geodesy, vol 94 n° 5 (May 2020)  

Public [article]  inJournal of geodesy > vol 94 n° 5 (May 2020) Titre : Self-tuning robust adjustment within multivariate regression time series models with vector-autoregressive random errors Type de document : Article/Communication Auteurs : Boris Kargoll, Auteur ; Gaël Kermarrec, Auteur ; Hamza Alkhatib, Auteur ; Johannes Korte, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] algorithme espérance-maximisation [Termes IGN] analyse vectorielle [Termes IGN] autorégression [Termes IGN] bruit blanc [Termes IGN] corrélation croisée normalisée [Termes IGN] erreur aléatoire [Termes IGN] méthode de Monte-Carlo [Termes IGN] modèle stochastique [Termes IGN] régression linéaire [Termes IGN] série temporelle [Termes IGN] station GPS [Termes IGN] valeur aberrante Résumé : (auteur) The iteratively reweighted least-squares approach to self-tuning robust adjustment of parameters in linear regression models with autoregressive (AR) and t-distributed random errors, previously established in Kargoll et al. (in J Geod 92(3):271–297, 2018. https://doi.org/10.1007/s00190-017-1062-6), is extended to multivariate approaches. Multivariate models are used to describe the behavior of multiple observables measured contemporaneously. The proposed approaches allow for the modeling of both auto- and cross-correlations through a vector-autoregressive (VAR) process, where the components of the white-noise input vector are modeled at every time instance either as stochastically independent t-distributed (herein called “stochastic model A”) or as multivariate t-distributed random variables (herein called “stochastic model B”). Both stochastic models are complementary in the sense that the former allows for group-specific degrees of freedom (df) of the t-distributions (thus, sensor-component-specific tail or outlier characteristics) but not for correlations within each white-noise vector, whereas the latter allows for such correlations but not for different dfs. Within the observation equations, nonlinear (differentiable) regression models are generally allowed for. Two different generalized expectation maximization (GEM) algorithms are derived to estimate the regression model parameters jointly with the VAR coefficients, the variance components (in case of stochastic model A) or the cofactor matrix (for stochastic model B), and the df(s). To enable the validation of the fitted VAR model and the selection of the best model order, the multivariate portmanteau test and Akaike’s information criterion are applied. The performance of the algorithms and of the white noise test is evaluated by means of Monte Carlo simulations. Furthermore, the suitability of one of the proposed models and the corresponding GEM algorithm is investigated within a case study involving the multivariate modeling and adjustment of time-series data at four GPS stations in the EUREF Permanent Network (EPN). Numéro de notice : A2020-291 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-020-01376-6 date de publication en ligne : 10/05/2020 En ligne : https://doi.org/10.1007/s00190-020-01376-6 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95120 [article]

A breakpoint detection in the mean model with heterogeneous variance on fixed time-intervals / Olivier Bock in Statistics and Computing, vol 29 n° 1 (February 2020)  

Public [article]  inStatistics and Computing > vol 29 n° 1 (February 2020) . - pp 1 - 13 Titre : A breakpoint detection in the mean model with heterogeneous variance on fixed time-intervals Type de document : Article/Communication Auteurs : Olivier Bock , Auteur ; Xavier Collilieux , Auteur ; François Guillamon, Auteur ; Emilie Lebarbier, Auteur ; Claire Pascal, Auteur Année de publication : 2020 Projets : 1-Pas de projet / Bock, Olivier Article en page(s) : pp 1 - 13 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] analyse de variance [Termes IGN] données GNSS [Termes IGN] inférence statistique [Termes IGN] méthode robuste [Termes IGN] points de rupture [Termes IGN] processus gaussien [Termes IGN] segmentation [Termes IGN] série temporelle [Termes IGN] variabilité Résumé : (Auteur) This work is motivated by an application for the homogenization of global navigation satellite system (GNSS)-derived integrated water vapour series. Indeed, these series are affected by abrupt changes due to equipment changes or environmental effects. The detection and correction of the series from these changes are a crucial step before any use for climate studies. In addition to these abrupt changes, it has been observed in the series a non-stationary of the variability. We propose in this paper a new segmentation model that is a breakpoint detection in the mean model of a Gaussian process with heterogeneous variance on known time intervals. In this segmentation case, the dynamic programming algorithm used classically to infer the breakpoints cannot be applied anymore. We propose a procedure in two steps: we first estimate robustly the variances and then apply the classical inference by plugging these estimators. The performance of our proposed procedure is assessed through simulation experiments. An application to real GNSS data is presented. Numéro de notice : A2020-368 Affiliation des auteurs : Géodésie+Ext (mi2018-2019) Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11222-019-09853-5 date de publication en ligne : 03/05/2019 En ligne : https://doi.org/10.1007/s11222-019-09853-5 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92571 [article]

Volcano-seismic transfer learning and uncertainty quantification with bayesian neural networks / Angel Bueno in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 2 (February 2020) . - pp Titre : Volcano-seismic transfer learning and uncertainty quantification with bayesian neural networks Type de document : Article/Communication Auteurs : Angel Bueno, Auteur ; Carmen Benitez, Auteur ; Silvio De Angelis, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] apprentissage profond [Termes IGN] classification bayesienne [Termes IGN] classification par réseau neuronal [Termes IGN] forme d'onde [Termes IGN] incertitude des données [Termes IGN] réseau bayesien [Termes IGN] réseau neuronal profond [Termes IGN] Russie [Termes IGN] séisme [Termes IGN] sismologie [Termes IGN] surveillance géologique [Termes IGN] volcanologie [Termes IGN] Washington (Etats-Unis ; état) Résumé : (auteur) Over the past few years, deep learning (DL) has emerged as an important tool in the fields of volcano and earthquake seismology. However, these methods have been applied without performing thorough analyses of the associated uncertainties. Here, we propose a solution to enhance volcano-seismic monitoring systems, through probabilistic Bayesian DL; we implement and demonstrate a workflow for waveform classification, rapid quantification of the associated uncertainty, and link these uncertainties to changes in volcanic unrest. Specifically, we introduce Bayesian neural networks (BNNs) to perform event identification, classification, and their estimated uncertainty on data gathered at two active volcanoes, Mount St. Helens, Washington, USA, and Bezymianny, Kamchatka, Russia. We demonstrate how BNNs achieve excellent performance (92.08%) in discriminating both the type of event and its origin when the two data sets are merged together, and no additional training information is provided. Finally, we demonstrate that the data representations learned by the BNNs are transferable across different eruptive periods. We also find that the estimated uncertainty is related to changes in the state of unrest at the volcanoes and propose that it could be used to gauge whether the learned models may be exported to other eruptive scenarios. Numéro de notice : A2020-094 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2941494 date de publication en ligne : 07/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2941494 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94657 [article]

Applied and computational statistics / Sorana D. Bolboacă (2020)  

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Bayesian inference on complicated data / Niansheng Tang (2020)  

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Development of new homogenisation methods for GNSS atmospheric data. Application to the analysis of climate trends and variability / Annarosa Quarello (2020)  

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Planar maps, random walks and circle packing / Asaf Nachmias (2020)  

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A factor model approach for the joint segmentation with between‐series correlation / Xavier Collilieux in Scandinavian Journal of Statistics, vol 46 n° 3 (September 2019)  

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Enhancing the predictability of least-squares collocation through the integration with least-squares-support vector machine / Hossam Talaat Elshambaky in Journal of applied geodesy, vol 13 n° 1 (January 2019)  

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Excel data analysis / Hector Guerrero (2019)  

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Exercices corrigés de géostatistique / Chantal de Fouquet (2019)

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Stochastic processes / Alexander Zeifman (2019)  

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Bayesian statistics and Monte Carlo methods / Karl Rudolf Koch in Journal of geodetic science, vol 8 n° 1 (January 2018)  

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