3 papers
stat.ML2020
Probabilistic Time Series Forecasting with Structured Shape and Temporal Diversity
Vincent Le Guen, Nicolas Thome
Probabilistic forecasting consists in predicting a distribution of possible future outcomes. In this paper, we address this problem for non-stationary time series, which is very ch…
cs.CV2020
Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction
Vincent Le Guen, Nicolas Thome
Leveraging physical knowledge described by partial differential equations (PDEs) is an appealing way to improve unsupervised video prediction methods. Since physics is too restrict…
stat.ML2019
Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models
Vincent Le Guen, Nicolas Thome
This paper addresses the problem of time series forecasting for non-stationary signals and multiple future steps prediction. To handle this challenging task, we introduce DILATE (D…