Abstract
We revisit a model for time-varying linear regression that assumes the unknown parameters evolve according to a linear dynamical system. Counterintuitively, we show that when the underlying dynamics are stable the parameters of this model can be estimated from data by combining just two ordinary least squares estimates. We offer a finite sample guarantee on the estimation error of our method and discuss certain advantages it has over Expectation-Maximization (EM), which is the main approach proposed by prior work.
| Original language | English |
|---|---|
| Pages (from-to) | 858-869 |
| Number of pages | 12 |
| Journal | Proceedings of Machine Learning Research |
| Volume | 168 |
| State | Published - 2022 |
| Externally published | Yes |
| Event | 4th Annual Learning for Dynamics and Control Conference, L4DC 2022 - Stanford, United States Duration: 23 Jun 2022 → 24 Jun 2022 |
Keywords
- Linear dynamical systems
- system identification
- time series
- time-varying regression