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Artykuły w czasopismach na temat "Prediction de séries temporelle"
Pérez, Fredy, i Velásquez Hermilson. "Análisis de cambio de régimen en series de tiempo no lienales utilizando modelos TAR". Lecturas de Economía, nr 61 (3.11.2009): 101–19. http://dx.doi.org/10.17533/udea.le.n61a2731.
Pełny tekst źródłaOszwald, Johan, i Valéry Gond. "De l'utilisation des séries temporelles SPOT-VEGETATION pour surveiller un front pionnier". BOIS & FORETS DES TROPIQUES 312, nr 312 (1.06.2012): 77. http://dx.doi.org/10.19182/bft2012.312.a20505.
Pełny tekst źródłaHoang, Cong-Tuan, Iouli Tchiguirinskaia, Daniel Schertzer i Shaun Lovejoy. "Caractéristiques multifractales et extrêmes de la précipitation à haute résolution, application à la détection du changement climatique". Revue des sciences de l’eau 27, nr 3 (15.12.2014): 205–16. http://dx.doi.org/10.7202/1027806ar.
Pełny tekst źródłaKOENIGUER, Elise, Jean-Marie Nicolas, Béatrice Pinel-Puyssegur, Jean-Michel Lagrange i Fabrice Janez. "Visualisation des changements sur séries temporelles radar : méthode REACTIV évaluée à l'échelle mondiale sous Google Earth Engine". Revue Française de Photogrammétrie et de Télédétection, nr 217-218 (21.09.2018): 99–108. http://dx.doi.org/10.52638/rfpt.2018.409.
Pełny tekst źródłaGagnon-Hébert, Amandine, Mikael Verrault, Adèle Jobin-Théberge, Jonathan Charest i Célyne Bastien. "Validation francophone des questionnaires de sommeil auprès des étudiants-athlètes du Québec". Psycause : revue scientifique étudiante de l'École de psychologie de l'Université Laval 9, nr 2 (6.10.2019): 25–26. http://dx.doi.org/10.51656/psycause.v9i2.20152.
Pełny tekst źródłaBoyard-Micheau, Joseph, i Pierre Camberlin. "Reconstitution de séries de pluies quotidiennes en Afrique de l’est : application aux caractéristiques des saisons des pluies". Climatologie 12 (2015): 83–105. http://dx.doi.org/10.4267/climatologie.1142.
Pełny tekst źródłaHallouz, Faiza, Mohamed Meddi i Gil Mahe. "Modification du régime hydroclimatique dans le bassin de l’Oued Mina (nord-ouest d’Algérie)". Revue des sciences de l’eau 26, nr 1 (18.03.2013): 33–38. http://dx.doi.org/10.7202/1014917ar.
Pełny tekst źródłaSOMÉ, Yélézouomin Stéphane Corentin, Alimata ZOROM, Wièmè SOMÉ i Pounyala Awa OUOBA. "Analyse de la dynamique de la végétation du Burkina Faso par utilisation de séries temporelles d’images FAPAR". International Journal of Progressive Sciences and Technologies 38, nr 1 (27.04.2023): 287. http://dx.doi.org/10.52155/ijpsat.v38.1.5191.
Pełny tekst źródłaEl Aoula, Rajae, Gil Mahé, Nadia Mhammdi, Abdellatif Ezzahouani, Ilias Kacimi i Kenza Khomsi. "Évolution du régime hydrologique dans le bassin versant du Bouregreg, Maroc". Proceedings of the International Association of Hydrological Sciences 384 (16.11.2021): 163–68. http://dx.doi.org/10.5194/piahs-384-163-2021.
Pełny tekst źródłaBoudhar, Abdelghani, Lahoucine Hanich, Ahmed Marchane, Lionel Jarlan i Abdelghani Chehbouni. "Apport des données FORMOSAT2 à la modélisation du contenu en eau du manteau neigeux du Haut Atlas marocain". Revue Française de Photogrammétrie et de Télédétection, nr 204 (8.04.2014): 51–56. http://dx.doi.org/10.52638/rfpt.2013.21.
Pełny tekst źródłaRozprawy doktorskie na temat "Prediction de séries temporelle"
Hmamouche, Youssef. "Prédiction des séries temporelles larges". Thesis, Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0480.
Pełny tekst źródłaNowadays, 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.
Pełny tekst źródłaNowadays, 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
Ziat, Ali Yazid. "Apprentissage de représentation pour la prédiction et la classification de séries temporelles". Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066324/document.
Pełny tekst źródłaThis thesis deals with the development of time series analysis methods. Our contributions focus on two tasks: time series forecasting and classification. Our first contribution presents a method of prediction and completion of multivariate and relational time series. The aim is to be able to simultaneously predict the evolution of a group of time series connected to each other according to a graph, as well as to complete the missing values in these series (which may correspond for example to a failure of a sensor during a given time interval). We propose to use representation learning techniques to forecast the evolution of the series while completing the missing values and taking into account the relationships that may exist between them. Extensions of this model are proposed and described: first in the context of the prediction of heterogeneous time series and then in the case of the prediction of time series with an expressed uncertainty. A prediction model of spatio-temporal series is then proposed, in which the relations between the different series can be expressed more generally, and where these can be learned.Finally, we are interested in the classification of time series. A joint model of metric learning and time-series classification is proposed and an experimental comparison is conducted
Ziat, Ali Yazid. "Apprentissage de représentation pour la prédiction et la classification de séries temporelles". Electronic Thesis or Diss., Paris 6, 2017. http://www.theses.fr/2017PA066324.
Pełny tekst źródłaThis thesis deals with the development of time series analysis methods. Our contributions focus on two tasks: time series forecasting and classification. Our first contribution presents a method of prediction and completion of multivariate and relational time series. The aim is to be able to simultaneously predict the evolution of a group of time series connected to each other according to a graph, as well as to complete the missing values in these series (which may correspond for example to a failure of a sensor during a given time interval). We propose to use representation learning techniques to forecast the evolution of the series while completing the missing values and taking into account the relationships that may exist between them. Extensions of this model are proposed and described: first in the context of the prediction of heterogeneous time series and then in the case of the prediction of time series with an expressed uncertainty. A prediction model of spatio-temporal series is then proposed, in which the relations between the different series can be expressed more generally, and where these can be learned.Finally, we are interested in the classification of time series. A joint model of metric learning and time-series classification is proposed and an experimental comparison is conducted
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.
Pełny tekst źródłaThis 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.
Pełny tekst źródłaThis 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
Arnoux, Thibaud. "Prédiction d'interactions dans les flots de liens. Combiner les caractéristiques structurelles et temporelles". Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS229.
Pełny tekst źródłaThe link stream formalism represent an approach allowing to capture the system dynamic while providing a framework to understand the system's behavior. A link stream is a sequence of triplet (t,u,v) indicating that an interaction occurred between u and v at time t. The importance of the system's dynamic during the prediction places it at the crossroads of link prediction in graphs and time series prediction. We will explore several formalizations of the problem of prediction in link streams. In the following we will study the activity prediction, that is to say predicting the number of interactions occurring in the future between each pair of nodes during a given period. We introduce the protocol, allowing to combine the data characteristics to predict the activity. We study the behavior of our protocol during several experiments on four datasets et evaluate the prediction quality. We will look at how the introduction of pair of nodes classes allows to preserve the link diversity in the prediction while improving the prediction. Our goal is to define a general prediction framework allowing in-depth studies of the relationship between temporal and structural characteristics in prediction tasks
Çinar, Yagmur Gizem. "Prédiction de séquences basée sur des réseaux de neurones récurrents dans le contexte des séries temporelles et des sessions de recherche d'information". Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAM079.
Pełny tekst źródłaThis thesis investigates challenges of sequence prediction in different scenarios such as sequence prediction using recurrent neural networks (RNNs) in the context of time series and information retrieval (IR) search sessions. Predicting the unknown values that follow some previously observed values is basically called sequence prediction.It is widely applicable to many domains where a sequential behavior is observed in the data. In this study, we focus on two different types of sequence prediction tasks: time series forecasting and next query prediction in an information retrieval search session.Time series often display pseudo-periods, i.e. time intervals with strong correlation between values of time series. Seasonal changes in weather time series or electricity usage at day and night time are some examples of pseudo-periods. In a forecasting scenario, pseudo-periods correspond to the difference between the positions of the output being predicted and specific inputs.In order to capture periods in RNNs, one needs a memory of the input sequence. Sequence-to-sequence RNNs (with attention mechanism) reuse specific (representations of) input values to predict output values. Sequence-to-sequence RNNs with an attention mechanism seem to be adequate for capturing periods. In this manner, we first explore the capability of an attention mechanism in that context. However, according to our initial analysis, a standard attention mechanism did not perform well to capture the periods. Therefore, we propose a period-aware content-based attention RNN model. This model is an extension of state-of-the-art sequence-to-sequence RNNs with attention mechanism and it is aimed to capture the periods in time series with or without missing values.Our experimental results with period-aware content-based attention RNNs show significant improvement on univariate and multivariate time series forecasting performance on several publicly available data sets.Another challenge in sequence prediction is the next query prediction. The next query prediction helps users to disambiguate their search query, to explore different aspects of the information they need or to form a precise and succint query that leads to higher retrieval performance. A search session is dynamic, and the information need of a user might change over a search session as a result of the search interactions. Furthermore, interactions of a user with a search engine influence the user's query reformulations. Considering this influence on the query formulations, we first analyze where the next query words come from? Using the analysis of the sources of query words, we propose two next query prediction approaches: a set view and a sequence view.The set view adapts a bag-of-words approach using a novel feature set defined based on the sources of next query words analysis. Here, the next query is predicted using learning to rank. The sequence view extends a hierarchical RNN model by considering the sources of next query words in the prediction. The sources of next query words are incorporated by using an attention mechanism on the interaction words. We have observed using sequence approach, a natural formulation of the problem, and exploiting all sources of evidence lead to better next query prediction
David, Etienne. "Time series forecasting models applied on large datasets with inclusion of external signals". Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAS002.
Pełny tekst źródłaTime series forecasting is a widespread mathematical problem in numerous sectors becoming a real challenge for existing methods of the literature where large datasets gathering thousands of time series and external signals are considered. A concrete illustration of this issue can be find in the fashion industry where its actors try to anticipate the evolution of thousands of garments to create their collections, analysing influencers and early adopters behaviours to propose the fashion of tomorrow.Using this application as a common thread, we present three contributions exploring different answers regarding the time series forecasting problem where large datasets and external signals are considered. A first answer is proposed with the introduction of a new hybrid model and the publication of a large dataset gathering 10000 fashion time series and influencers external signals. A second approach is then studied with theoretical work done on hidden Markov models with external signals. Finally, a last answer is proposed with the introduction of a new method mixing the inner workings of hidden Markov model and neural networks.Results presented in this three contribution highlighted several elements of answer. Firstly, neural networks are decisive to deal with large datasets and they are particularly well designed to leverage external signals. Secondly, hidden Markov models with external signals are also strong methods that can capture complex dependencies between time series and their external signals. However, they fail at handling large datasets as a model has to be trained for each new time series. Finally, inspired by the striking results of hidden Markov models with external signals, we reveal that introducing hidden processes in neural-network-based models can help them explore large datasets more deeply, model a richer variety of behaviour and leverage more finely external signals
Andreux, Mathieu. "Foveal autoregressive neural time-series modeling". Electronic Thesis or Diss., Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE073.
Pełny tekst źródłaThis dissertation studies unsupervised time-series modelling. We first focus on the problem of linearly predicting future values of a time-series under the assumption of long-range dependencies, which requires to take into account a large past. We introduce a family of causal and foveal wavelets which project past values on a subspace which is adapted to the problem, thereby reducing the variance of the associated estimators. We then investigate under which conditions non-linear predictors exhibit better performances than linear ones. Time-series which admit a sparse time-frequency representation, such as audio ones, satisfy those requirements, and we propose a prediction algorithm using such a representation. The last problem we tackle is audio time-series synthesis. We propose a new generation method relying on a deep convolutional neural network, with an encoder-decoder architecture, which allows to synthesize new realistic signals. Contrary to state-of-the-art methods, we explicitly use time-frequency properties of sounds to define an encoder with the scattering transform, while the decoder is trained to solve an inverse problem in an adapted metric
Książki na temat "Prediction de séries temporelle"
Unit roots in economic time series. Basingstoke: Palgrave Macmillan, 2004.
Znajdź pełny tekst źródłaHarvey, A. C. Time series models. Wyd. 2. New York: Harvester Wheatsheaf, 1992.
Znajdź pełny tekst źródłaHarvey, A. C. Time series models. Wyd. 2. New York: Harvester Wheatsheaf, 1993.
Znajdź pełny tekst źródłaTime series models. Wyd. 2. Cambridge, Mass: MIT Press, 1993.
Znajdź pełny tekst źródłaPatterson, Kerry. Unit Roots in Economic Time Series (Palgrave Texts in Econometrics). Palgrave Macmillan, 2008.
Znajdź pełny tekst źródłaPatterson, Kerry, i K. D. Patterson. Unit Roots in Economic Time Series (Palgrave Texts in Econometrics). Palgrave Macmillan, 2008.
Znajdź pełny tekst źródłaCzęści książek na temat "Prediction de séries temporelle"
GARZELLI, Andrea, i Claudia ZOPPETTI. "Analyse multitemporelle d’images Sentinel-1/2 pour le suivi de l’utilisation des sols". W Détection de changements et analyse des séries temporelles d’images 1, 221–45. ISTE Group, 2022. http://dx.doi.org/10.51926/iste.9056.ch8.
Pełny tekst źródłaBRETON, Justine. "Comprendre les épidémies des séries arthuriennes au regard de la pandémie de 2020". W Les épidémies au prisme des SHS, 45–54. Editions des archives contemporaines, 2022. http://dx.doi.org/10.17184/eac.5989.
Pełny tekst źródłaStreszczenia konferencji na temat "Prediction de séries temporelle"
Pastor, Ruam E. R. C., Jair P. de Sales, Adriano F. C. Filho i Paulo S. G. de Mattos Neto. "Improving crime prediction through ensembles". W Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/eniac.2023.234600.
Pełny tekst źródłaGiudice Batista de Araujo Porto, Vítor, i Leonardo Rocha Olivi. "Prediction of Brazilian Electric Energy Price Using Recurrent Artificial Neural Networks and Correction Filter". W Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1678.
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