Letteratura scientifica selezionata sul tema "Agrégation des séries temporelles"
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Articoli di riviste sul tema "Agrégation des séries temporelles":
Bonneuil, Noël. "Traitement des données manquantes dans les séries issues des registres paroissiaux". Population Vol. 53, n. 1 (1 gennaio 1998): 249–70. http://dx.doi.org/10.3917/popu.p1998.53n1-2.0270.
Nijman e Palm. "Séries temporelles incomplètes en modélisation macroéconomique". Cahiers du Séminaire d'Économétrie, n. 27 (1985): 141. http://dx.doi.org/10.2307/20075587.
Lafrance, 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. 197 (21 aprile 2014): 10–16. http://dx.doi.org/10.52638/rfpt.2012.78.
Teixeira, A. "Les séries chronologiques ou séries temporelles : présentation et principes d’analyse". Revue des Maladies Respiratoires 22, n. 3 (giugno 2005): 493–95. http://dx.doi.org/10.1016/s0761-8425(05)85582-2.
Inglada, 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. 219-220 (19 gennaio 2020): 71–72. http://dx.doi.org/10.52638/rfpt.2019.468.
Jayet, Pierre-Alain. "Quelques notions sur l'analyse spectrale des séries temporelles". Histoire & Mesure 6, n. 1 (1991): 7–29. http://dx.doi.org/10.3406/hism.1991.1381.
Renaut, Didier. "Les séries temporelles de produits satellitaires passées au crible". La Météorologie 8, n. 88 (2015): 4. http://dx.doi.org/10.4267/2042/56354.
Dronne, Yves, e Christophe Tavéra. "Substitution dans l'alimentation animale : l'apport des modèles de séries temporelles". Cahiers d'Economie et sociologie rurales 23, n. 1 (1992): 63–86. http://dx.doi.org/10.3406/reae.1992.1306.
Gond, 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. 2 (febbraio 1997): 179–88. http://dx.doi.org/10.1016/s0764-4469(97)85010-x.
Hili, Ouagnina. "Sur l'estimation des modèles autorégressifs d'ordre multiple de séries temporelles". Comptes Rendus de l'Académie des Sciences - Series I - Mathematics 332, n. 8 (aprile 2001): 755–59. http://dx.doi.org/10.1016/s0764-4442(01)01897-3.
Tesi sul tema "Agrégation des séries temporelles":
Sànchez, Pérez Andrés. "Agrégation de prédicteurs pour des séries temporelles, optimalité dans un contexte localement stationnaire". Thesis, Paris, ENST, 2015. http://www.theses.fr/2015ENST0051/document.
This thesis regroups our results on dependent time series prediction. The work is divided into three main chapters where we tackle different problems. The first one is the aggregation of predictors of Causal Bernoulli Shifts using a Bayesian approach. The second one is the aggregation of predictors of what we define as sub-linear processes. Locally stationary time varying autoregressive processes receive a particular attention; we investigate an adaptive prediction scheme for them. In the last main chapter we study the linear regression problem for a general class of locally stationary processes
Sànchez, Pérez Andrés. "Agrégation de prédicteurs pour des séries temporelles, optimalité dans un contexte localement stationnaire". Electronic Thesis or Diss., Paris, ENST, 2015. http://www.theses.fr/2015ENST0051.
This thesis regroups our results on dependent time series prediction. The work is divided into three main chapters where we tackle different problems. The first one is the aggregation of predictors of Causal Bernoulli Shifts using a Bayesian approach. The second one is the aggregation of predictors of what we define as sub-linear processes. Locally stationary time varying autoregressive processes receive a particular attention; we investigate an adaptive prediction scheme for them. In the last main chapter we study the linear regression problem for a general class of locally stationary processes
Gaillard, Pierre. "Contributions à l’agrégation séquentielle robuste d’experts : Travaux sur l’erreur d’approximation et la prévision en loi. Applications à la prévision pour les marchés de l’énergie". Thesis, Paris 11, 2015. http://www.theses.fr/2015PA112133/document.
We are interested in online forecasting of an arbitrary sequence of observations. At each time step, some experts provide predictions of the next observation. Then, we form our prediction by combining the expert forecasts. This is the setting of online robust aggregation of experts. The goal is to ensure a small cumulative regret. In other words, we want that our cumulative loss does not exceed too much the one of the best expert. We are looking for worst-case guarantees: no stochastic assumption on the data to be predicted is made. The sequence of observations is arbitrary. A first objective of this work is to improve the prediction accuracy. We investigate several possibilities. An example is to design fully automatic procedures that can exploit simplicity of the data whenever it is present. Another example relies on working on the expert set so as to improve its diversity. A second objective of this work is to produce probabilistic predictions. We are interested in coupling the point prediction with a measure of uncertainty (i.e., interval forecasts,…). The real world applications of the above setting are multiple. Indeed, very few assumptions are made on the data. Besides, online learning that deals with data sequentially is crucial to process big data sets in real time. In this thesis, we carry out for EDF several empirical studies of energy data sets and we achieve good forecasting performance
Gagnon, Jean-François. "Prévision humaine de séries temporelles". Doctoral thesis, Université Laval, 2014. http://hdl.handle.net/20.500.11794/25243.
Hmamouche, Youssef. "Prédiction des séries temporelles larges". Thesis, Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0480.
Nowadays, 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.
Nowadays, 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.
Nowakowski, Samuel. "Détection de défauts dans les séries temporelles". Nancy 1, 1989. http://www.theses.fr/1989NAN10074.
Haykal, Vanessa. "Modélisation des séries temporelles par apprentissage profond". Thesis, Tours, 2019. http://www.theses.fr/2019TOUR4019.
Time 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.
Time 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
Libri sul tema "Agrégation des séries temporelles":
Aragon, Yves. Séries temporelles avec R. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0208-4.
Gourieroux, Christian. Séries temporelles et modèles dynamiques. Paris: Economica, 1990.
Thionbiano, Taladidia. ECONOMÉTRIE DES SÉRIES TEMPORELLES - Cours et exercices. Paris: Editions L'Harmattan, 2008.
Meuriot, Véronique. Une histoire des concepts des séries temporelles. Louvain-la-Neuve: Harmattan-Academia, 2012.
Aragon, Yves. Séries temporelles avec R: Méthodes et cas. Paris: Springer Paris, 2011.
Pawłowski, Adam. Séries temporelles en linguistique: Avec application à l'attribution de textes, Romain Gary et Emile Ajar. Paris: H. Champion, 1998.
Enders, Walter. Applied econometric time series. 2a ed. Hoboken, NJ: Wiley, 2003.
Enders, Walter. Applied econometric time series. New York: John Wiley, 1995.
Franses, Philip Hans. Time series models for business and economic forecasting. Cambridge, UK: Cambridge University Press, 1998.
Aragon, Yves. Séries temporelles avec R. EDP Sciences, 2020. http://dx.doi.org/10.1051/978-2-7598-1994-2.
Capitoli di libri sul tema "Agrégation 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.
Aragon, 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.
Aragon, 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.
Aragon, 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.
"Bibliographie". In Analyse des séries temporelles, 345–52. Dunod, 2016. http://dx.doi.org/10.3917/dunod.bourb.2016.01.0345.
"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.
"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.
"AVANT-PROPOS". In Séries temporelles avec R, xi—xvi. EDP Sciences, 2020. http://dx.doi.org/10.1051/978-2-7598-1994-2-003.
"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.
"Index". In Séries temporelles avec R, 261–64. EDP Sciences, 2020. http://dx.doi.org/10.1051/978-2-7598-1994-2-017.
Atti di convegni sul tema "Agrégation des 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.
Rapporti di organizzazioni sul tema "Agrégation des 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.
Nicault, 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.