- Mehryar Mohri
- Vitaly Kuznetsov
In this paper, we introduce and analyze Discriminative State Space Models for forecasting non-stationary time series. We provide data-dependent generalization guarantees for learning these models based on recently introduced notion of discrep- ancy. We provide an in-depth analysis of complexity of such models. Finally, we also study generalization ability of several structural risk minimization approaches to this problem and provide efficient implementation for one of them which is based on a convex objective.