Energy-based temporal neural networks for imputing missing values

Imputing missing values in high dimensional time series is a difficult problem. There have been some approaches to the problem [11,8] where neural architectures were trained as probabilistic models of the data. However, we argue that this approach is not optimal. We propose to view temporal neural networks with latent variables as energy-based models and train them for missing value recovery directly. In this paper we introduce two energy-based models. The first model is based on a one dimensional convolution and the second model utilizes a recurrent neural network. We demonstrate how ideas from the energy-based learning framework can be used to train these models to recover missing values. The models are evaluated on a motion capture dataset

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