Combining a learned Koopman linearization with a learned Kalman filter inside a VAE produces a probabilistic forecaster that beats existing methods on most tested short- and long-horizon datasets.
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$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting
Combining a learned Koopman linearization with a learned Kalman filter inside a VAE produces a probabilistic forecaster that beats existing methods on most tested short- and long-horizon datasets.