# Kalman Recursions Aggregated Online

arXiv: https://arxiv.org/abs/2002.12173 · HTML: https://mlsanity.com/paper/2002.12173/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2020.

## Built on by (most cited first, 3 of 3)

- [CRPS Learning](https://mlsanity.com/paper/2102.00968/) (2021)
- [Stochastic Online Convex Optimization. Application to probabilistic time series forecasting](https://mlsanity.com/paper/2102.00729/) (2021)
- [Adaptive time series forecasting with markovian variance switching](https://mlsanity.com/paper/2402.14684/) (2024)

## Abstract (arXiv)

In this article, we aim at improving the prediction of expert aggregation by using the underlying properties of the models that provide expert predictions. We restrict ourselves to the case where expert predictions come from Kalman recursions, fitting state-space models. By using exponential weights, we construct different algorithms of Kalman recursions Aggregated Online (KAO) that compete with the best expert or the best convex combination of experts in a more or less adaptive way. We improve the existing results on expert aggregation literature when the experts are Kalman recursions by taking advantage of the second-order properties of the Kalman recursions. We apply our approach to Kalman recursions and extend it to the general adversarial expert setting by state-space modeling the errors of the experts. We apply these new algorithms to a real dataset of electricity consumption and show how it can improve forecast performances comparing to other exponentially weighted average procedures.