Modeling Multivariate Spatio-Temporal Data with Identifiable Variational Autoencoders: A Comparative Study of Algorithms and Their Application to Meteorological Data

This research paper introduces novel identifiable variational autoencoder (iVAE) methods for separating independent latent components from multivariate spatio-temporal data, addressing limitations of existing methods by handling nonlinear mixing and nonstationary variances.