Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models

International audienceThis paper addresses the problem of time series forecasting for non-stationarysignals and multiple future steps prediction. To handle this challenging task, weintroduce DILATE (DIstortion Loss including shApe and TimE), a new objectivefunction for training deep neural networks. DILATE aims at accurately predictingsudden changes, and explicitly incorporates two terms supporting precise shapeand temporal change detection. We introduce a differentiable loss function suitablefor training deep neural nets, and provide a custom back-prop implementation forspeeding up optimization. We also introduce a variant of DILATE, which providesa smooth generalization of temporally-constrained Dynamic Time Warping (DTW).Experiments carried out on various non-stationary datasets reveal the very goodbehaviour of DILATE compared to models trained with the standard Mean SquaredError (MSE) loss function, and also to DTW and variants. DILATE is also agnosticto the choice of the model, and we highlight its benefit for training fully connectednetworks as well as specialized recurrent architectures, showing its capacity toimprove over state-of-the-art trajectory forecasting approaches

Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction

Leveraging physical knowledge described by partial differential equations (PDEs) is an appealing way to improve unsupervised video prediction methods. Since physics is too restrictive for describing the full visual content of generic videos, we introduce PhyDNet, a two-branch deep architecture, which explicitly disentangles PDE dynamics from unknown complementary information. A second contribution is to propose a new recurrent physical cell (PhyCell), inspired from data assimilation techniques, for performing PDE-constrained prediction in latent space. Extensive experiments conducted on four various datasets show the ability of PhyDNet to outperform state-of-the-art methods. Ablation studies also highlight the important gain brought out by both disentanglement and PDE-constrained prediction. Finally, we show that PhyDNet presents interesting features for dealing with missing data and long-term forecasting

Amélioration génétique de l'hévéa en Amérique du Sud : Brésil, Guyane, Guadeloupe, Martinique. Mission du 22 novembre au 22 décembre 1995

Cette mission en Amérique du Sud rend compte d'une réflexion sur l'hévéaculture soumise au Microcydus, à partir d'observations effectuées sur les deux plantations brésiliennes de Michelin (PMB à Bahia et PEM dans le Mato Grosso) et sur le site de Combi en Guyane, ainsi que de la visite des collections d'hévéas au CIRAD en Guyane et Guadeloupe, accessoirement en Martinique. Un rapport spécifique (n. CP 514) rend compte des discussions du 3ème Comité de Pilotage de la convention Cirad-Michelin dans laquelle s'inscrit cette mission. Un transfert de 14 clones de Guyane en Guadeloupe et un transfert de 49 clones de Guadeloupe au Guatemala ont été réalisés au cours de cette mission