Academic literature on the topic 'Classement des séries temporelles'
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Journal articles on the topic "Classement des séries temporelles"
García-Perez, Miguel A. "La décennie 1989-1999 dans la psychologie espagnole : analyse de la recherche en statistiques, méthodologie et théorie psychométrique." Bulletin de psychologie 56, no. 464 (2003): 167–78. http://dx.doi.org/10.3406/bupsy.2003.15211.
Full textBonneuil, Noël. "Traitement des données manquantes dans les séries issues des registres paroissiaux." Population Vol. 53, no. 1 (January 1, 1998): 249–70. http://dx.doi.org/10.3917/popu.p1998.53n1-2.0270.
Full textNijman and Palm. "Séries temporelles incomplètes en modélisation macroéconomique." Cahiers du Séminaire d'Économétrie, no. 27 (1985): 141. http://dx.doi.org/10.2307/20075587.
Full textLafrance, Bruno, Xavier Lenot, Caroline Ruffel, Patrick Cao, and Thierry Rabaute. "Outils de prétraitements des images optiques Kalideos." Revue Française de Photogrammétrie et de Télédétection, no. 197 (April 21, 2014): 10–16. http://dx.doi.org/10.52638/rfpt.2012.78.
Full textTeixeira, A. "Les séries chronologiques ou séries temporelles : présentation et principes d’analyse." Revue des Maladies Respiratoires 22, no. 3 (June 2005): 493–95. http://dx.doi.org/10.1016/s0761-8425(05)85582-2.
Full textInglada, 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, no. 219-220 (January 19, 2020): 71–72. http://dx.doi.org/10.52638/rfpt.2019.468.
Full textJayet, Pierre-Alain. "Quelques notions sur l'analyse spectrale des séries temporelles." Histoire & Mesure 6, no. 1 (1991): 7–29. http://dx.doi.org/10.3406/hism.1991.1381.
Full textRenaut, Didier. "Les séries temporelles de produits satellitaires passées au crible." La Météorologie 8, no. 88 (2015): 4. http://dx.doi.org/10.4267/2042/56354.
Full textPerrenoud, Alfred, and Frédéric Sardet. "Les causes de décès aux XVIF et XVIIF siècles à Genève : nosologie et pathocénose." Gesnerus 48, no. 3-4 (November 25, 1991): 269–86. http://dx.doi.org/10.1163/22977953-0480304004.
Full textDronne, Yves, and Christophe Tavéra. "Substitution dans l'alimentation animale : l'apport des modèles de séries temporelles." Cahiers d'Economie et sociologie rurales 23, no. 1 (1992): 63–86. http://dx.doi.org/10.3406/reae.1992.1306.
Full textDissertations / Theses on the topic "Classement des séries temporelles"
Al, Saleh Mohammed. "SPADAR : Situation-aware and proactive analytics for dynamic adaptation in real time." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG060.
Full textAlthough radiation level is a serious concern that requires continuous monitoring, many existing systems are designed to perform this task. Radiation Early Warning System (REWS) is one of these systems which monitors the gamma radiation level in the air. Such a system requires high manual intervention, depends totally on experts' analysis, and has some shortcomings that can be risky sometimes. In this thesis, the RIMI (Refining Incoming Monitored Incidents) approach will be introduced, which aims to improve this system while becoming more autonomous while keeping the final decision to the experts. A new method is presented which will help in changing this system to become more intelligent while learning from past incidents of each specific system
Gagnon, Jean-François. "Prévision humaine de séries temporelles." Doctoral thesis, Université Laval, 2014. http://hdl.handle.net/20.500.11794/25243.
Full textHmamouche, Youssef. "Prédiction des séries temporelles larges." Thesis, Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0480.
Full textNowadays, 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
Hmamouche, Youssef. "Prédiction des séries temporelles larges." Electronic Thesis or Diss., Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0480.
Full textNowadays, 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.
Full textNowakowski, Samuel. "Détection de défauts dans les séries temporelles." Nancy 1, 1989. http://www.theses.fr/1989NAN10074.
Full textHaykal, Vanessa. "Modélisation des séries temporelles par apprentissage profond." Thesis, Tours, 2019. http://www.theses.fr/2019TOUR4019.
Full textTime 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.
Full textTime 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.
Full textThis 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
Frambourg, Cédric. "Apprentissage d'appariements pour la discrimination de séries temporelles." Phd thesis, Université de Grenoble, 2013. http://tel.archives-ouvertes.fr/tel-00948989.
Full textBooks on the topic "Classement des séries temporelles"
Aragon, Yves. Séries temporelles avec R. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0208-4.
Full textGourieroux, Christian. Séries temporelles et modèles dynamiques. Paris: Economica, 1990.
Find full textThionbiano, Taladidia. ECONOMÉTRIE DES SÉRIES TEMPORELLES - Cours et exercices. Paris: Editions L'Harmattan, 2008.
Find full textMeuriot, Véronique. Une histoire des concepts des séries temporelles. Louvain-la-Neuve: Harmattan-Academia, 2012.
Find full textservice), SpringerLink (Online, ed. Séries temporelles avec R: Méthodes et cas. Paris: Springer Paris, 2011.
Find full textPawłowski, Adam. Séries temporelles en linguistique: Avec application à l'attribution de textes, Romain Gary et Emile Ajar. Paris: H. Champion, 1998.
Find full textApplied econometric time series. 2nd ed. Hoboken, NJ: Wiley, 2003.
Find full textEnders, Walter. Applied econometric time series. New York: John Wiley, 1995.
Find full textTime series models for business and economic forecasting. Cambridge, UK: Cambridge University Press, 1998.
Find full textAragon, Yves. Séries temporelles avec R. EDP Sciences, 2020. http://dx.doi.org/10.1051/978-2-7598-1994-2.
Full textBook chapters on the topic "Classement des 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.
Full textAragon, 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.
Full textAragon, 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.
Full textAragon, 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.
Full text"Bibliographie." In Analyse des séries temporelles, 345–52. Dunod, 2016. http://dx.doi.org/10.3917/dunod.bourb.2016.01.0345.
Full text"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.
Full text"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.
Full text"AVANT-PROPOS." In Séries temporelles avec R, xi—xvi. EDP Sciences, 2020. http://dx.doi.org/10.1051/978-2-7598-1994-2-003.
Full text"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.
Full text"Index." In Séries temporelles avec R, 261–64. EDP Sciences, 2020. http://dx.doi.org/10.1051/978-2-7598-1994-2-017.
Full textConference papers on the topic "Classement des séries temporelles"
LAFON, Virginie, Arthur ROBINET, Tatiana DONNAY, David DOXARAN, Bertrand LUBAC, Eric MANEUX, Aldo SOTTOLICHIO, and 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.
Full textReports on the topic "Classement des séries temporelles"
Perreault, L., A. Nicault, É. Boucher, D. Arseneault, and 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.
Full textNicault, A., L. Cournoyer, T. Labarre, and 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.
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