Viking: Variational Bayesian Variance Tracking

Understand

We consider the problem of time series forecasting in an adaptive setting.

• We focus on the inference of state-space models under unknown and potentially time-varying noise variances.
• We introduce an augmented model in which the variances are represented as auxiliary gaussian latent variables in a tracking mode.
• As variances are nonnegative, a transformation is chosen and applied to these latent variables.