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This paper introduces a new latent variable generative model able to handlehigh dimensional longitudinal data and relying on variational inference. Thetime dependency between the observations of an input sequence is modelled usingnormalizing flows over the associated latent variables. The proposed method canbe used to generate either fully synthetic longitudinal sequences ortrajectories that are conditioned on several data in a sequence anddemonstrates good robustness properties to missing data. We test the model on 6datasets of different complexity and show that it can achieve better likelihoodestimates than some competitors as well as more reliable missing dataimputation. A code is made available at\url{https://github.com/clementchadebec/variational_inference_for_longitudinal_data}.

Variational Inference for Longitudinal Data Using Normalizing Flows