# Worst-case Prediction Performance Analysis of the Kalman Filter

arXiv: https://arxiv.org/abs/1611.02050 · HTML: https://mlsanity.com/paper/1611.02050/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2016.

## Built on by (most cited first, 1 of 1)

- [Regret-Optimal Filtering for Prediction and Estimation](https://mlsanity.com/paper/2101.10357/) (2021)

## Abstract (arXiv)

In this paper, we study the prediction performance of the Kalman filter (KF) in a worst-case, minimax setting as studied in online machine learning, information - and game theory. The aim is to predict the sequence of observations almost as well as the best reference predictor (comparator) sequence in a comparison class. We prove worst-case bounds on the cumulative squared prediction errors using a priori knowledge about the complexity of reference predictor sequence. In fact, the performance of the KF is derived as a function of the performance of the best reference predictor and the total amount of drift occurs in the schedule of the best comparator.