Variational Learning for Switching State-Space Models

摘要

We introduce a new statistical model for time series that iteratively seg- ments data into regimes with approximately linear dynamics and learns the parameters of each of these linear regimes. This model combines and generalizes two of the most widely used stochastic time-series models- hidden Markov models and linear dynamical systems-and is closely related to models that fare widely used in the control and econometrics literatures.