Model and Data Uncertainty for Satellite Time Series Forecasting with Deep Recurrent Models
Marc Rußwurm, Mohsin Ali, Xiao Xiang Zhu, Yarin Gal, Marco Körner
Abstract
Deep Learning is often criticized as being a black-box method that provides accurate predictions, but a limited explanation of the underlying processes and no indication when to not trust those predictions. Equipping existing deep learning models with an (general) notion of uncertainty can help mitigate both these issues. The Bayesian deep learning community has developed model-agnostic methodology to estimate both data and model uncertainty that can be implemented on top of existing deep learning models. In this work, we test this methodology for deep recurrent satellite time series forecasting and test its assumptions on data and model uncertainty. We tested its effectiveness on an application on climate change where the activity of seasonal vegetation decreased over multiple years.