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Статті в журналах з теми "Prévision des séries temporelles":
Leprêtre, Alain, and Patrice Carpentier. "Une méthode simple de prévision des tendances appliquée aux séries temporelles de qualité des eaux courantes." Comptes Rendus de l'Académie des Sciences - Series III - Sciences de la Vie 320, no. 5 (May 1997): 407–11. http://dx.doi.org/10.1016/s0764-4469(97)85029-9.
CHEIFETZ, N., L. GUERY, K. DELABRE, and V. HEIM. "Anticipation de crues sur la Seine, la Marne et l’Oise pour protéger la production d’eau potable." 11, no. 11 (November 22, 2021): 45–52. http://dx.doi.org/10.36904/tsm/202111045.
Fayolle, Jacky, and Alexandre Mathis. "Structure des taux d'intérêt et mouvements cycliques des économies américaine et française." Revue de l'OFCE 49, no. 2 (June 1, 1994): 125–48. http://dx.doi.org/10.3917/reof.p1994.49n1.0125.
Bonneuil, 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.
Nijman and Palm. "Séries temporelles incomplètes en modélisation macroéconomique." Cahiers du Séminaire d'Économétrie, no. 27 (1985): 141. http://dx.doi.org/10.2307/20075587.
Lafrance, 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.
Teixeira, 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.
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, no. 219-220 (January 19, 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, no. 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, no. 88 (2015): 4. http://dx.doi.org/10.4267/2042/56354.
Дисертації з теми "Prévision des séries temporelles":
Gagnon, Jean-François. "Prévision humaine de séries temporelles." Doctoral thesis, Université Laval, 2014. http://hdl.handle.net/20.500.11794/25243.
Boné, Romuald. "Réseaux de neurones récurrents pour la prévision de séries temporelles." Tours, 2000. http://www.theses.fr/2000TOUR4003.
Cherif, Aymen. "Réseaux de neurones, SVM et approches locales pour la prévision de séries temporelles." Thesis, Tours, 2013. http://www.theses.fr/2013TOUR4003/document.
Time series forecasting is a widely discussed issue for many years. Researchers from various disciplines have addressed it in several application areas : finance, medical, transportation, etc. In this thesis, we focused on machine learning methods : neural networks and SVM. We have also been interested in the meta-methods to push up the predictor performances, and more specifically the local models. In a divide and conquer strategy, the local models perform a clustering over the data sets before different predictors are affected into each obtained subset. We present in this thesis a new algorithm for recurrent neural networks to use them as local predictors. We also propose two novel clustering techniques suitable for local models. The first is based on Kohonen maps, and the second is based on binary trees
Huard, Malo. "Apprentissage et prévision séquentiels : bornes uniformes pour le regret linéaire et séries temporelles hiérarchiques." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM009.
This work presents some theoretical and practical contributions to the prediction of arbitrary sequences. In this domain, forecasting takes place sequentially at the same time as learning. At each step, the model is fitted on the past data in order to predict the next observation. The goal of this model is to make the best possible predictions, i.e. those that minimize their deviations from the observations, which are made a posteriori. Sequential learning methods are evaluated by their regret, which measures how close strategies are to the best possible, known only after all the data is available. In this thesis, we extend the set of weights vectors a method is compared to when doing sequential linear regression. We have adapted an existing algorithm by improving its theoretical guarantees allowing it to be compared to any constant linear combination without restriction on the norm of its mixing weights. A second work consisted in extending sequential forecasting methods when forcasted data is organized in a hierarchy. We tested these hierarchical methods on two practical applications, household power consumption prediction and demand forecasts in e-commerce
Lefieux, Vincent. "Modèles semi-paramétriques appliqués à la prévision des séries temporelles : cas de la consommation d’électricité." Phd thesis, Rennes 2, 2007. https://theses.hal.science/tel-00179866/fr/.
Réseau de Transport d’Electricité (RTE), in charge of operating the French electric transportation grid, needs an accurate forecast of the power consumption in order to operate it correctly. The forecasts used everyday result from a model combining a nonlinear parametric regression and a SARIMA model. In order to obtain an adaptive forecasting model, nonparametric forecasting methods have already been tested without real success. In particular, it is known that a nonparametric predictor behaves badly with a great number of explanatory variables, what is commonly called the curse of dimensionality. Recently, semiparametric methods which improve the pure nonparametric approach have been proposed to estimate a regression function. Based on the concept of ”dimension reduction”, one those methods (called MAVE : Moving Average -conditional- Variance Estimate) can apply to time series. We study empirically its effectiveness to predict the future values of an autoregressive time series. We then adapt this method, from a practical point of view, to forecast power consumption. We propose a partially linear semiparametric model, based on the MAVE method, which allows to take into account simultaneously the autoregressive aspect of the problem and the exogenous variables. The proposed estimation procedure is practicaly efficient
Lefieux, Vincent. "Modèles semi-paramétriques appliqués à la prévision des séries temporelles. Cas de la consommation d'électricité." Phd thesis, Université Rennes 2, 2007. http://tel.archives-ouvertes.fr/tel-00179866.
Tatsa, Sylvestre. "Modélisation et prévision de la consommation horaire d'électricité au Québec : comparaison de méthodes de séries temporelles." Thesis, Université Laval, 2014. http://www.theses.ulaval.ca/2014/30329/30329.pdf.
This work explores the dynamics of residential electricity consumption in Quebec using hourly data from January 2006 to December 2010. We estimate three standard autoregressive models in time series analysis: the Holt-Winters exponential smoothing, the seasonal ARIMA model (SARIMA) and the seasonal ARIMA model with exogenous variables (SARIMAX). For the latter model, we focus on the effect of climate variables (temperature, relative humidity and dew point and cloud cover). Climatic factors have a significant impact on the short-term electricity consumption. The intra-sample and out-of-sample predictive performance of each model is evaluated with various adjustment indicators. Three out-of-sample time horizons are tested: 24 hours (one day), 72 hours (three days) and 168 hours (1 week). The SARIMA model provides the best out-of-sample predictive performance of 24 hours. The SARIMAX model reveals the most powerful out-of-sample time horizons of 72 and 168 hours. Additional research is needed to obtain predictive models fully satisfactory from a methodological point of view. Keywords: modeling, electricity, Holt-Winters, SARIMA, SARIMAX.
Vroman, Philippe. "Prédiction des séries temporelles en milieu incertain : application à la prévision de ventes dans la distribution textile." Lille 1, 2000. http://www.theses.fr/2000LIL10207.
Melzi, Fateh. "Fouille de données pour l'extraction de profils d'usage et la prévision dans le domaine de l'énergie." Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC1123/document.
Nowadays, countries are called upon to take measures aimed at a better rationalization of electricity resources with a view to sustainable development. Smart Metering solutions have been implemented and now allow a fine reading of consumption. The massive spatio-temporal data collected can thus help to better understand consumption behaviors, be able to forecast them and manage them precisely. The aim is to be able to ensure "intelligent" use of resources to consume less and consume better, for example by reducing consumption peaks or by using renewable energy sources. The thesis work takes place in this context and aims to develop data mining tools in order to better understand electricity consumption behaviors and to predict solar energy production, then enabling intelligent energy management.The first part of the thesis focuses on the classification of typical electrical consumption behaviors at the scale of a building and then a territory. In the first case, an identification of typical daily power consumption profiles was conducted based on the functional K-means algorithm and a Gaussian mixture model. On a territorial scale and in an unsupervised context, the aim is to identify typical electricity consumption profiles of residential users and to link these profiles to contextual variables and metadata collected on users. An extension of the classical Gaussian mixture model has been proposed. This allows exogenous variables such as the type of day (Saturday, Sunday and working day,...) to be taken into account in the classification, thus leading to a parsimonious model. The proposed model was compared with classical models and applied to an Irish database including both electricity consumption data and user surveys. An analysis of the results over a monthly period made it possible to extract a reduced set of homogeneous user groups in terms of their electricity consumption behaviors. We have also endeavoured to quantify the regularity of users in terms of consumption as well as the temporal evolution of their consumption behaviors during the year. These two aspects are indeed necessary to evaluate the potential for changing consumption behavior that requires a demand response policy (shift in peak consumption, for example) set up by electricity suppliers.The second part of the thesis concerns the forecast of solar irradiance over two time horizons: short and medium term. To do this, several approaches have been developed, including autoregressive statistical approaches for modelling time series and machine learning approaches based on neural networks, random forests and support vector machines. In order to take advantage of the different models, a hybrid model combining the different models was proposed. An exhaustive evaluation of the different approaches was conducted on a large database including four locations (Carpentras, Brasilia, Pamplona and Reunion Island), each characterized by a specific climate as well as weather parameters: measured and predicted using NWP models (Numerical Weather Predictions). The results obtained showed that the hybrid model improves the results of photovoltaic production forecasts for all locations
Zuo, Jingwei. "Apprentissage de représentations et prédiction pour des séries-temporelles inter-dépendantes." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG038.
Time series is a common data type that has been applied to enormous real-life applications, such as financial analysis, medical diagnosis, environmental monitoring, astronomical discovery, etc. Due to its complex structure, time series raises several challenges in their data processing and mining. The representation of time series plays a key role in data mining tasks and machine learning algorithms for time series. Yet, a few methods consider the interrelation that may exist between different time series when building the representation. Moreover, the time series mining requires considering not only the time series' characteristics in terms of data complexity but also the concrete application scenarios where the data mining task is performed to build task-specific representations.In this thesis, we will study different time series representation approaches that can be used in various time series mining tasks, while capturing the relationships among them. We focus specifically on modeling the interrelations between different time series when building the representations, which can be the temporal relationship within each data source or the inter-variable relationship between various data sources. Accordingly, we study the time series collected from various application contexts under different forms. First, considering the temporal relationship between the observations, we learn the time series in a dynamic streaming context, i.e., time series stream, for which the time series data is continuously generated from the data source. Second, for the inter-variable relationship, we study the multivariate time series (MTS) with data collected from multiple data sources. Finally, we study the MTS in the Smart City context, when each data source is given a spatial position. The MTS then becomes a geo-located time series (GTS), for which the inter-variable relationship requires more modeling efforts with the external spatial information. Therefore, for each type of time series data collected from distinct contexts, the interrelations between the time series observations are emphasized differently, on the temporal or (and) variable axis.Apart from the data complexity from the interrelations, we study various machine learning tasks on time series in order to validate the learned representations. The high-level learning tasks studied in this thesis consist of time series classification, semi-supervised time series learning, and time series forecasting. We show how the learned representations connect with different time series learning tasks under distinct application contexts. More importantly, we conduct the interdisciplinary study on time series by leveraging real-life challenges in machine learning tasks, which allows for improving the learning model's performance and applying more complex time series scenarios.Concretely, for these time series learning tasks, our main research contributions are the following: (i) we propose a dynamic time series representation learning model in the streaming context, which considers both the characteristics of time series and the challenges in data streams. We claim and demonstrate that the Shapelet, a shape-based time series feature, is the best representation in such a dynamic context; (ii) we propose a semi-supervised model for representation learning in multivariate time series (MTS). The inter-variable relationship over multiple data sources is modeled in a real-life context, where the data annotations are limited; (iii) we design a geo-located time series (GTS) representation learning model for Smart City applications. We study specifically the traffic forecasting task, with a focus on the missing-value treatment within the forecasting algorithm
Книги з теми "Prévision des séries temporelles":
Franses, Philip Hans. Time series models for business and economic forecasting. Cambridge, UK: Cambridge University Press, 1998.
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. 2nd ed. Hoboken, NJ: Wiley, 2003.
Enders, Walter. Applied econometric time series. New York: John Wiley, 1995.
Bowerman, Bruce L. Time series forecasting: Unified concepts and computer implementation. 2nd ed. Boston: Duxbury Press, 1987.
Частини книг з теми "Prévision 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.
Тези доповідей конференцій з теми "Prévision 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.
Звіти організацій з теми "Prévision 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.
Nicault, 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.