Literatura científica selecionada sobre o tema "Regroupement de séries temporelles"
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Artigos de revistas sobre o assunto "Regroupement de séries temporelles"
SAMBOU, Al Housseyny, e Samba DIOP. "L'intégration monétaire en Afrique : existe-t-il une synchronisation des cycles économiques entre et au sein des communautés économiques régionales ?" Revue d’Economie Théorique et Appliquée 13, n.º 1 (30 de junho de 2023): 135–50. http://dx.doi.org/10.62519/reta.v13n1a8.
Texto completo da fonteBonneuil, Noël. "Traitement des données manquantes dans les séries issues des registres paroissiaux". Population Vol. 53, n.º 1 (1 de janeiro de 1998): 249–70. http://dx.doi.org/10.3917/popu.p1998.53n1-2.0270.
Texto completo da fonteNijman e Palm. "Séries temporelles incomplètes en modélisation macroéconomique". Cahiers du Séminaire d'Économétrie, n.º 27 (1985): 141. http://dx.doi.org/10.2307/20075587.
Texto completo da fonteLafrance, Bruno, Xavier Lenot, Caroline Ruffel, Patrick Cao e Thierry Rabaute. "Outils de prétraitements des images optiques Kalideos". Revue Française de Photogrammétrie et de Télédétection, n.º 197 (21 de abril de 2014): 10–16. http://dx.doi.org/10.52638/rfpt.2012.78.
Texto completo da fonteTeixeira, A. "Les séries chronologiques ou séries temporelles : présentation et principes d’analyse". Revue des Maladies Respiratoires 22, n.º 3 (junho de 2005): 493–95. http://dx.doi.org/10.1016/s0761-8425(05)85582-2.
Texto completo da fonteInglada, Jordi. "Lettre : Utilisation conjointe de séries temporelles d'images optiques et radar pour le suivi des surfaces agricoles". Revue Française de Photogrammétrie et de Télédétection, n.º 219-220 (19 de janeiro de 2020): 71–72. http://dx.doi.org/10.52638/rfpt.2019.468.
Texto completo da fonteJayet, Pierre-Alain. "Quelques notions sur l'analyse spectrale des séries temporelles". Histoire & Mesure 6, n.º 1 (1991): 7–29. http://dx.doi.org/10.3406/hism.1991.1381.
Texto completo da fonteRenaut, Didier. "Les séries temporelles de produits satellitaires passées au crible". La Météorologie 8, n.º 88 (2015): 4. http://dx.doi.org/10.4267/2042/56354.
Texto completo da fonteDronne, Yves, e Christophe Tavéra. "Substitution dans l'alimentation animale : l'apport des modèles de séries temporelles". Cahiers d'Economie et sociologie rurales 23, n.º 1 (1992): 63–86. http://dx.doi.org/10.3406/reae.1992.1306.
Texto completo da fonteGond, Valéry, Jacques Fontès e Philippe Loudjani. "Dynamique des biomes africains par l'analyse de séries temporelles satellitales". Comptes Rendus de l'Académie des Sciences - Series III - Sciences de la Vie 320, n.º 2 (fevereiro de 1997): 179–88. http://dx.doi.org/10.1016/s0764-4469(97)85010-x.
Texto completo da fonteTeses / dissertações sobre o assunto "Regroupement de séries temporelles"
Gagnon, Jean-François. "Prévision humaine de séries temporelles". Doctoral thesis, Université Laval, 2014. http://hdl.handle.net/20.500.11794/25243.
Texto completo da fonteHmamouche, Youssef. "Prédiction des séries temporelles larges". Thesis, Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0480.
Texto completo da fonteNowadays, storage and data processing systems are supposed to store and process large time series. As the number of variables observed increases very rapidly, their prediction becomes more and more complicated, and the use of all the variables poses problems for classical prediction models.Univariate prediction models are among the first models of prediction. To improve these models, the use of multiple variables has become common. Thus, multivariate models and become more and more used because they consider more information.With the increase of data related to each other, the application of multivariate models is also questionable. Because the use of all existing information does not necessarily lead to the best predictions. Therefore, the challenge in this situation is to find the most relevant factors among all available data relative to a target variable.In this thesis, we study this problem by presenting a detailed analysis of the proposed approaches in the literature. We address the problem of prediction and size reduction of massive data. We also discuss these approaches in the context of Big Data.The proposed approaches show promising and very competitive results compared to well-known algorithms, and lead to an improvement in the accuracy of the predictions on the data used.Then, we present our contributions, and propose a complete methodology for the prediction of wide time series. We also extend this methodology to big data via distributed computing and parallelism with an implementation of the prediction process proposed in the Hadoop / Spark environment
Hugueney, Bernard. "Représentations symboliques de longues séries temporelles". Paris 6, 2003. http://www.theses.fr/2003PA066161.
Texto completo da fonteNowakowski, Samuel. "Détection de défauts dans les séries temporelles". Nancy 1, 1989. http://www.theses.fr/1989NAN10074.
Texto completo da fonteHaykal, Vanessa. "Modélisation des séries temporelles par apprentissage profond". Thesis, Tours, 2019. http://www.theses.fr/2019TOUR4019.
Texto completo da fonteTime series prediction is a problem that has been addressed for many years. In this thesis, we have been interested in methods resulting from deep learning. It is well known that if the relationships between the data are temporal, it is difficult to analyze and predict accurately due to non-linear trends and the existence of noise specifically in the financial and electrical series. From this context, we propose a new hybrid noise reduction architecture that models the recursive error series to improve predictions. The learning process fusessimultaneouslyaconvolutionalneuralnetwork(CNN)andarecurrentlongshort-term memory network (LSTM). This model is distinguished by its ability to capture globally a variety of hybrid properties, where it is able to extract local signal features, to learn long-term and non-linear dependencies, and to have a high noise resistance. The second contribution concerns the limitations of the global approaches because of the dynamic switching regimes in the signal. We present a local unsupervised modification with our previous architecture in order to adjust the results by adapting the Hidden Markov Model (HMM). Finally, we were also interested in multi-resolution techniques to improve the performance of the convolutional layers, notably by using the variational mode decomposition method (VMD)
Jabbari, Ali. "Encodage visuel composite pour les séries temporelles". Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAM035/document.
Texto completo da fonteTime series are one of the most common types of recorded data in various scientific, industrial, and financial domains. Depending on the context, time series analysis are used for a variety of purposes: forecasting, estimation, classification, and trend and event detection. Thanks to the outstanding capabilities of human visual perception, visualization remains one of the most powerful tools for data analysis, particularly for time series. With the increase in data sets' volume and complexity, new visualization techniques are clearly needed to improve data analysis. They aim to facilitate visual analysis in specified situations, tasks, or for unguided exploratory analysis.Visualization is based upon visual mapping, which consists in association of data values to visual channels, e.g. position, size, and color of the graphical elements. In this regard, the most familiar form of time series visualization, i.e. line charts, consists in a mapping of data values to the vertical position of the line. However, a single visual mapping is not suitable for all situations and analytical objectives.Our goal is to introduce alternatives to the conventional visual mapping and find situations in which, the new approach compensate for the simplicity and familiarity of the existing techniques. We present a review of the existing literature on time series visualization and then, we focus on the existing approaches to visual mapping.Next, we present our contributions. Our first contribution is a systematic study of a "composite" visual mapping which consists in using combinations of visual channels to communicate different facets of a time series. By means of several user studies, we compare our new visual mappings with an existing reference technique and we measure users' speed and accuracy in different analytical tasks. Our results show that the new visual designs lead to analytical performances close to those of the existing techniques without being unnecessarily complex or requiring training. Also, some of the proposed mappings outperform the existing techniques in space constraint situations. Space efficiency is of great importance to simultaneous visualization of large volumes of data or visualization on small screens. Both scenarios are among the current challenges in information visualization
Assaad, Charles. "Découvertes de relations causales entre séries temporelles". Electronic Thesis or Diss., Université Grenoble Alpes, 2021. http://www.theses.fr/2021GRALM019.
Texto completo da fonteThis thesis aims to give a broad coverage of central concepts and principles of causation and in particular the ones involved in the emerging approaches to causal discovery from time series.After reviewing concepts and algorithms, we first present a new approach that infer a summary graph of the causal system underlying the observational time series while relaxing the idealized setting of equal sampling rates and discuss the assumptions underlying its validity. The gist of our proposal lies in the introduction of the causal temporal mutual information measure that can detect the independence and the conditional independence between two time series, and in making an apparent connection between entropy and the probability raising principle that can be used for building new rules for the orientation of the direction of causation. Moreover, through the development of this base method, we propose several extensions, namely to handle hidden confounders, to infer a window causal graph given a summary graph, and to consider sequences instead of time series.Secondly, we focus on the discovery of causal relations from a statistical distribution that is not entirely faithful to the real causal graph and on distinguishing a common cause from an intermediate cause even in the absence of a time indicator. The key aspect of our answer to this problem is the reliance on the additive noise principle to infer a directed supergraph that contains the causal graph. To converge toward the causal graph, we use in a second step a new measure called the temporal causation entropy that prunes for each node of the directed supergraph, the parents that are conditionally independent of their child. Furthermore, we explore complementary extensions of our second base method that involve a pairwise strategy which reduces through multitask learning and a denoising technique, the number of functions that need to be estimated. We perform an extensive experimental comparison of the proposed algorithms on both synthetic and real datasets and demonstrate their promising practical performance: gaining in time complexity while preserving accuracy
Claeys, Emmanuelle. "Clusterisation incrémentale, multicritères de données hétérogènes pour la personnalisation d’expérience utilisateur". Thesis, Strasbourg, 2019. http://www.theses.fr/2019STRAD039.
Texto completo da fonteIn many activity sectors (health, online sales,...) designing from scratch an optimal solution for a defined problem (finding a protocol to increase the cure rate, designing a web page to promote the purchase of one or more products,...) is often very difficult or even impossible. In order to face this difficulty, designers (doctors, web designers, production engineers,...) often work incrementally by successive improvements of an existing solution. However, defining the most relevant changes remains a difficult problem. Therefore, a solution adopted more and more frequently is to compare constructively different alternatives (also called variations) in order to determine the best one by an A/B Test. The idea is to implement these alternatives and compare the results obtained, i.e. the respective rewards obtained by each variation. To identify the optimal variation in the shortest possible time, many test methods use an automated dynamic allocation strategy. Its allocate the tested subjects quickly and automatically to the most efficient variation, through a learning reinforcement algorithms (as one-armed bandit methods). These methods have shown their interest in practice but also limitations, including in particular a latency time (i.e. a delay between the arrival of a subject to be tested and its allocation) too long, a lack of explicitness of choices and the integration of an evolving context describing the subject's behaviour before being tested. The overall objective of this thesis is to propose a understable generic A/B test method allowing a dynamic real-time allocation which take into account the temporals static subjects’s characteristics
Frambourg, Cédric. "Apprentissage d'appariements pour la discrimination de séries temporelles". Phd thesis, Université de Grenoble, 2013. http://tel.archives-ouvertes.fr/tel-00948989.
Texto completo da fonteEl, Ghini Ahmed. "Contribution à l'identification de modèles de séries temporelles". Lille 3, 2008. http://www.theses.fr/2008LIL30017.
Texto completo da fonteThis PhD dissertation consists of two parts dealing with the probelms of identification and selection in econometrics. Two mains topics are considered : (1) time series model identification by using (inverse) autocorrelation and (inverse) partial autocorrelation functions ; (2) estimation of inverse autocorrelation function in the framework of nonlinear tima series. The two parts are summarized below. In the first part of this work, we consider time series model identification y using (inverse) autocorrelation and (inverse) partial autocorrelation functions. We construct statistical tests based on estimators of these functions and establish their asymptotic distribution. Using Bahadur and Pitman approaches, we compare the performance of (inverse) autocorelations and (inverse) partial autocorrelations in detecting the order of moving average and autoregressive model. Next, we study the identification of the inverse process of an ARMA model and their probalistic properties. Finally, we characterize the time reversibility by means of the dual and inverse processes. The second part is devoted to estimation of the inverse autocorrelation function in the framework of nonlinear time series. Undes some regularity conditions, we study the asymptotic properties of empirical inverse autocorrelations for stationary and strongly mixing process. We establish the consistency and the asymptotic normality of the estimators. Next, we consider the case of linear process with GARCH errors and obtain means of some examples that the standard formula can be misleading if the generating process is non linear. Finally, we apply our previous results to prove the asymptotic normality of the parameter estimates of weak moving average. Our results are illustrated by Monte Carlo experiments and real data experiences
Livros sobre o assunto "Regroupement de séries temporelles"
Aragon, Yves. Séries temporelles avec R. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0208-4.
Texto completo da fonteGourieroux, Christian. Séries temporelles et modèles dynamiques. Paris: Economica, 1990.
Encontre o texto completo da fonteThionbiano, Taladidia. ECONOMÉTRIE DES SÉRIES TEMPORELLES - Cours et exercices. Paris: Editions L'Harmattan, 2008.
Encontre o texto completo da fonteMeuriot, Véronique. Une histoire des concepts des séries temporelles. Louvain-la-Neuve: Harmattan-Academia, 2012.
Encontre o texto completo da fonteservice), SpringerLink (Online, ed. Séries temporelles avec R: Méthodes et cas. Paris: Springer Paris, 2011.
Encontre o texto completo da fontePawłowski, Adam. Séries temporelles en linguistique: Avec application à l'attribution de textes, Romain Gary et Emile Ajar. Paris: H. Champion, 1998.
Encontre o texto completo da fonteApplied econometric time series. 2a ed. Hoboken, NJ: Wiley, 2003.
Encontre o texto completo da fonteEnders, Walter. Applied econometric time series. New York: John Wiley, 1995.
Encontre o texto completo da fonteTime series models for business and economic forecasting. Cambridge, UK: Cambridge University Press, 1998.
Encontre o texto completo da fonteAragon, Yves. Séries temporelles avec R. EDP Sciences, 2020. http://dx.doi.org/10.1051/978-2-7598-1994-2.
Texto completo da fonteCapítulos de livros sobre o assunto "Regroupement de séries temporelles"
Aragon, Yves. "Séries temporelles non stationnaires". In Pratique R, 97–120. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0208-4_5.
Texto completo da fonteAragon, Yves. "R pour les séries temporelles". In Pratique R, 21–38. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0208-4_2.
Texto completo da fonteAragon, Yves. "Démarche de base en séries temporelles". In Pratique R, 1–20. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0208-4_1.
Texto completo da fonteAragon, Yves. "Modèles de base en séries temporelles". In Pratique R, 57–95. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0208-4_4.
Texto completo da fonte"Bibliographie". In Analyse des séries temporelles, 345–52. Dunod, 2016. http://dx.doi.org/10.3917/dunod.bourb.2016.01.0345.
Texto completo da fonte"Chapitre 2 R pour les séries temporelles". In Séries temporelles avec R, 21–38. EDP Sciences, 2020. http://dx.doi.org/10.1051/978-2-7598-1994-2-005.
Texto completo da fonte"Chapitre 6 Lissage exponentiel". In Séries temporelles avec R, 123–34. EDP Sciences, 2020. http://dx.doi.org/10.1051/978-2-7598-1994-2-009.
Texto completo da fonte"AVANT-PROPOS". In Séries temporelles avec R, xi—xvi. EDP Sciences, 2020. http://dx.doi.org/10.1051/978-2-7598-1994-2-003.
Texto completo da fonte"Chapitre 8 Trafic mensuel de l’aéroport de Toulouse-Blagnac". In Séries temporelles avec R, 149–72. EDP Sciences, 2020. http://dx.doi.org/10.1051/978-2-7598-1994-2-011.
Texto completo da fonte"Index". In Séries temporelles avec R, 261–64. EDP Sciences, 2020. http://dx.doi.org/10.1051/978-2-7598-1994-2-017.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Regroupement de séries temporelles"
LAFON, Virginie, Arthur ROBINET, Tatiana DONNAY, David DOXARAN, Bertrand LUBAC, Eric MANEUX, Aldo SOTTOLICHIO e Olivier HAGOLLE. "RIVERCOLOR : chaîne de traitement des séries temporelles LANDSAT, SPOT et MODIS dédiée à la cartographie des matières en suspension en zone estuarienne". In Journées Nationales Génie Côtier - Génie Civil. Editions Paralia, 2014. http://dx.doi.org/10.5150/jngcgc.2014.067.
Texto completo da fonteRelatórios de organizações sobre o assunto "Regroupement de séries temporelles"
Perreault, L., A. Nicault, É. Boucher, D. Arseneault e F. Gennaretti. Analyse des changements de régimes dans les séries temporelles issues de la dendrochronologie. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328084.
Texto completo da fonteNicault, A., L. Cournoyer, T. Labarre e Y. Bégin. Analyse des relations entre le climat et les séries temporelles de densité de cerne. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328074.
Texto completo da fonte