We consider the problem of variational Bayesian inference in a latent variable model where a (possibly complex) observed stochastic process is governed by the solution of a latent stochastic differential equation (SDE).

However, estimating and optimizingα-divergences require to use importance sampling, which may havelarge orinfinite variance due to heavy tails ofimportance weights.

The problem of (point) forecastingunivariate time series is considered.