Spatio-Temporal Neural Networks for Space-Time Series Forecasting and Relations Discovery

International audienceWe introduce a dynamical spatio-temporal model formalized as a recurrent neural network for forecasting time series of spatial processes, i.e. series of observations sharing temporal and spatial dependencies. The model learns these dependencies through a structured latent dynamical component, while a decoder predicts the observations from the latent representations. We consider several variants of this model, corresponding to different prior hypothesis about the spatial relations between the series. The model is evaluated and compared to state-of-the-art baselines, on a variety of forecasting problems representative of different application areas: epidemiology, geo-spatial statistics and car-traffic prediction. Besides these evaluations, we also describe experiments showing the ability of this approach to extract relevant spatial relations

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

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.Nous nous intéressons au développement de méthodes qui répondent aux difficultés posées par l’analyse des séries temporelles. Nos contributions se focalisent sur deux tâches : la prédiction de séries temporelles et la classification de séries temporelles. Notre première contribution présente une méthode de prédiction et de complétion de séries temporelles multivariées et relationnelles. Le but est d’être capable de prédire simultanément l’évolution d’un ensemble de séries temporelles reliées entre elles selon un graphe, ainsi que de compléter les valeurs manquantes dans ces séries (pouvant correspondre par exemple à une panne d’un capteur pendant un intervalle de temps donné). On se propose d’utiliser des techniques d’apprentissage de représentation pour prédire l’évolution des séries considérées tout en complétant les valeurs manquantes et prenant en compte les relations qu’il peut exister entre elles. Des extensions de ce modèle sont proposées et décrites : d’abord dans le cadre de la prédiction de séries temporelles hétérogènes puis dans le cas de la prédiction de séries temporelles avec une incertitude exprimée. Un modèle de prédiction de séries spatio-temporelles est ensuiteproposé, avec lequel les relations entre les différentes séries peuvent être exprimées de manière plus générale, et où ces dernières peuvent être apprises.Enfin, nous nous intéressons à la classification de séries temporelles. Un modèle d’apprentissage joint de métrique et de classification de séries est proposé et une comparaison expérimentale est menée

Representation Learning for Time-Series Forecasting and Classification

Nous nous intéressons au développement de méthodes qui répondent aux difficultés posées par l’analyse des séries temporelles. Nos contributions se focalisent sur deux tâches : la prédiction de séries temporelles et la classification de séries temporelles. Notre première contribution présente une méthode de prédiction et de complétion de séries temporelles multivariées et relationnelles. Le but est d’être capable de prédire simultanément l’évolution d’un ensemble de séries temporelles reliées entre elles selon un graphe, ainsi que de compléter les valeurs manquantes dans ces séries (pouvant correspondre par exemple à une panne d’un capteur pendant un intervalle de temps donné). On se propose d’utiliser des techniques d’apprentissage de représentation pour prédire l’évolution des séries considérées tout en complétant les valeurs manquantes et prenant en compte les relations qu’il peut exister entre elles. Des extensions de ce modèle sont proposées et décrites : d’abord dans le cadre de la prédiction de séries temporelles hétérogènes puis dans le cas de la prédiction de séries temporelles avec une incertitude exprimée. Un modèle de prédiction de séries spatio-temporelles est ensuiteproposé, avec lequel les relations entre les différentes séries peuvent être exprimées de manière plus générale, et où ces dernières peuvent être apprises.Enfin, nous nous intéressons à la classification de séries temporelles. Un modèle d’apprentissage joint de métrique et de classification de séries est proposé et une comparaison expérimentale est menée.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

Spatio-temporal neural networks for space-time data modeling and relation discovery

International audienceWe introduce a dynamical spatio-temporal model formalized as a recurrent neural network for modeling time series of spatial processes, i.e. series of observations sharing temporal and spatial dependencies. The model learns these dependencies through a structured latent dynamical component, while a decoder predicts the observations from the latent representations. We consider several variants of this model, corresponding to different prior hypothesis about the spatial relations between the series. The model is used for the tasks of forecasting and data imputation. It is evaluated and compared to state-of-the-art baselines, on a variety of forecasting and imputation problems representative of different application areas: epidemiology, geo-spatial statistics and car-traffic prediction. The experiments also show that this approach is able to learn relevant spatial relations without prior information

Joint prediction of road-traffic and parking occupancy over a city with representation learning

journey planning services begins to include real time traffic forecast features in order to compute more accurate routing along the journey, adaptive traffic control systems can also benefit from this prediction so as to minimize traffic congestion. But these two systems dedicated to end user and road traffic management authorities could also benefits from other information, and particularly from parking availability prediction since cruising for parking spot represents a significant part of urban traffic: when looking for a parking, drivers must guess where to go, and if they are wrong, may face long distances to find the next location, resulting in considerable time loss and a worsening of traffic congestion. We focus on the simultaneous prediction of traffic and parking availability. Our approach relay on machine learning techniques and more precisely on representation learning methods: each road and car-park is represented by a vector in a common large dimensional space which captures both structural and dynamical information about the observed phenomenon. Such a model is thus able to jointly capture the spatio-temporal correlations between parking and traffic resulting in a high performance prediction system. The results of our experiments on the Grand Lyon (France) urban area show the effectiveness of our approach compared to state of the art methods

Joint prediction of road-traffic and parking occupancy over a city with representation learning

journey planning services begins to include real time traffic forecast features in order to compute more accurate routing along the journey, adaptive traffic control systems can also benefit from this prediction so as to minimize traffic congestion. But these two systems dedicated to end user and road traffic management authorities could also benefits from other information, and particularly from parking availability prediction since cruising for parking spot represents a significant part of urban traffic: when looking for a parking, drivers must guess where to go, and if they are wrong, may face long distances to find the next location, resulting in considerable time loss and a worsening of traffic congestion. We focus on the simultaneous prediction of traffic and parking availability. Our approach relay on machine learning techniques and more precisely on representation learning methods: each road and car-park is represented by a vector in a common large dimensional space which captures both structural and dynamical information about the observed phenomenon. Such a model is thus able to jointly capture the spatio-temporal correlations between parking and traffic resulting in a high performance prediction system. The results of our experiments on the Grand Lyon (France) urban area show the effectiveness of our approach compared to state of the art methods