Deep generation network for multivariate spatio-temporal data based on separated attention

Abstract

The multivariate spatio-temporal data contains complex spatio-temporal background and channel coupling information. Effective extraction of these features is crucial for data generation. In this paper, a separated multiple attention network is proposed, which can capture the correlation of multiple types of variables in the same space-time, different spaces at the same time, and different times in the same space. Meanwhile, a new multiscale loss processing method based on homoscedasticity uncertainty and the assumption of gaussian loss distribution is proposed to balance the numerical scale of each channel loss in training. The experiment shows that our method has better performance and robustness than the classical machine learning model and higher computational efficiency than the physical model. It can generate multivariate intermittent spatial-temporal fields with a maximum lead time of 3 days and multivariate continuous spatial-temporal fields with a maximum lead time of 7 days.