The robustness of driving force signals extracted by slow feature analysis

Siyi Wang, Yongqiang Chen, Ying Mei and Wenping He

Chaos, Solitons & Fractals, 2023, vol. 171, issue C

Abstract: The slow feature analysis (SFA) can be used to extract potential driving force signals from a non-stationary time series. In recent years, although several studies have explored the applicability of SFA, the robustness of SFA to extract the driving forces is still an open question which is crucial to using it in practice. Based on this,the influence of three factors on the ability of SFA to extract the driving force signals is investigated in this paper, including spike noise, missing data, and sample size. Three classical chaotical models are used in our studies, namely, Logistic model, Henon model, and Lorenz model. The results show that SFA has a relatively strong anti-noise ability, but spike noise will cause high-frequency fluctuations in the extracted signals and weaken the extraction ability of SFA. Different degrees of missing data have a non-negligible influence on the performance of SFA to extract external forcing. As the degree of missing data increases, the extraction ability of SFA will be significantly reduced. However, the number of missing data from different dynamical systems has different effects on the extraction ability of SFA. In addition, the length of the sample size has a negligible effect on the ability of SFA to extract the driving force signals. The present research provides a more robust reference for the application of SFA in practice.

Keywords: Slow feature analysis; Driving force; Logistic map; Henon map; Lorenz model (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:171:y:2023:i:c:s096007792300348x

DOI: 10.1016/j.chaos.2023.113447

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