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The robustness of driving force signals extracted by slow feature analysis

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  • Wang, Siyi
  • Chen, Yongqiang
  • Mei, Ying
  • He, Wenping

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.

Suggested Citation

  • Wang, Siyi & Chen, Yongqiang & Mei, Ying & He, Wenping, 2023. "The robustness of driving force signals extracted by slow feature analysis," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    • Handle: RePEc:eee:chsofr:v:171:y:2023:i:c:s096007792300348x
    DOI: 10.1016/j.chaos.2023.113447
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    References listed on IDEAS

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    1. Tang, Li-Hong & Bai, Yu-Long & Yang, Jie & Lu, Ya-Ni, 2020. "A hybrid prediction method based on empirical mode decomposition and multiple model fusion for chaotic time series," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    2. Matsuoka, Chihiro & Hiraide, Koichi, 2012. "Entropy estimation of the Hénon attractor," Chaos, Solitons & Fractals, Elsevier, vol. 45(6), pages 805-809.
    3. Silva, Emily & Peacock-Lopez, Enrique, 2017. "Seasonality and the logistic map," Chaos, Solitons & Fractals, Elsevier, vol. 95(C), pages 152-156.
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