Apprentissage de représentation pour la prédiction et la classification de séries temporelles

Ali Yazid Ziat 1
1 MLIA - Machine Learning and Information Access
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : This 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.
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Ali Yazid Ziat. Apprentissage de représentation pour la prédiction et la classification de séries temporelles. Réseau de neurones [cs.NE]. Université Pierre et Marie Curie - Paris VI, 2017. Français. ⟨NNT : 2017PA066324⟩. ⟨tel-01724156⟩

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