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Journal Articles Journal of Statistical Mechanics: Theory and Experiment Year : 2023

Filtering time-dependent covariance matrices using time-independent eigenvalues

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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Cite

Christian Bongiorno, Damien Challet, Grégoire Loeper. Filtering time-dependent covariance matrices using time-independent eigenvalues. Journal of Statistical Mechanics: Theory and Experiment, 2023, 2023 (2), pp.023402. ⟨10.1088/1742-5468/acb7ed⟩. ⟨hal-03481441⟩
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