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Modelling nonstationary dynamics

Author

Listed:
  • Széliga, M.I.
  • Verdes, P.F.
  • Granitto, P.M.
  • Ceccatto, H.A.

Abstract

We incorporate the use of validation data to cope with noisy records in a neural network-based method for modelling the dynamics of slowly changing nonstationary systems. As a byproduct, we obtain a precise criterion to find the optimal value of a required internal hyperparameter. Testing these ideas on a controlled problem shows that the resulting algorithm is able to outperform previous methods in the literature, allowing a more accurate modelling of nonstationary dynamics.

Suggested Citation

  • Széliga, M.I. & Verdes, P.F. & Granitto, P.M. & Ceccatto, H.A., 2003. "Modelling nonstationary dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 327(1), pages 190-194.
    • Handle: RePEc:eee:phsmap:v:327:y:2003:i:1:p:190-194
    DOI: 10.1016/S0378-4371(03)00475-8
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    Cited by:

    1. Zhang, Feng & Yang, Peicai & Fraedrich, Klaus & Zhou, Xiuji & Wang, Geli & Li, Jiangnan, 2017. "Reconstruction of driving forces from nonstationary time series including stationary regions and application to climate change," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 337-343.

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