Variational Augmented Invertible Koopman Autoencoder for probabilistic time series forecasting
Neural Koopman autoencoder models have been shown to successfully build a latent embedding with linear dynamics for arbitrary dynamical systems, enabling strong performance in long-term time series forecasting. However, these models usually work in a deterministic setting, which does not allow the quantification of the...
Anthony Frion, L. Drumetz, G. Tochon et al.
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