Model-free Online Learning for the Kalman Filter: Forgetting Factor and Logarithmic Regret
This work addresses performance degradation in model-free Kalman filtering for unknown systems, offering an incremental improvement with a sharper regret bound.
The paper tackles the problem of online prediction for unknown linear stochastic systems by introducing exponential forgetting to balance the regression model, achieving a logarithmic regret bound of O(log^3 N).
We consider the problem of online prediction for an unknown, non-explosive linear stochastic system. With a known system model, the optimal predictor is the celebrated Kalman filter. In the case of unknown systems, existing approaches based on recursive least squares and its variants may suffer from degraded performance due to the highly imbalanced nature of the regression model. This imbalance can easily lead to overfitting and thus degrade prediction accuracy. We tackle this problem by injecting an inductive bias into the regression model via {exponential forgetting}. While exponential forgetting is a common wisdom in online learning, it is typically used for re-weighting data. In contrast, our approach focuses on balancing the regression model. This achieves a better trade-off between {regression} and {regularization errors}, and simultaneously reduces the {accumulation error}. With new proof techniques, we also provide a sharper logarithmic regret bound of $O(\log^3 N)$, where $N$ is the number of observations.