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Cleaning the covariance matrix of strongly nonstationary systems with time-independent eigenvalues

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Abstract

We propose a data-driven way to clean covariance matrices in strongly nonstationary systems. Our method rests on long-term averaging of optimal eigenvalues obtained from temporally contiguous covariance matrices, which encodes the average influence of the future on present eigenvalues. This zero-th order approximation outperforms optimal methods designed for stationary systems.

Dates and versions

hal-03481441 , version 1 (15-12-2021)

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Christian Bongiorno, Damien Challet, Grégoire Loeper. Cleaning the covariance matrix of strongly nonstationary systems with time-independent eigenvalues. 2021. ⟨hal-03481441⟩
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