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Time Series Determinism Recognition by LSTM Model

Author

Listed:
  • Janusz Miśkiewicz

    (Institute of Theoretical Physics, University of Wrocław, pl. M. Borna 6, 50-204 Wrocław, Poland
    Physics and Biophysics Department, Wrocław University of Environmental and Life Sciences, ul. Norwida 25, 50-375 Wrocław, Poland)

  • Paweł Witkowicz

    (Institute of Theoretical Physics, University of Wrocław, pl. M. Borna 6, 50-204 Wrocław, Poland)

Abstract

The problem of time series determinism measurement is investigated. It is shown that a deep learning model can be used as a determinism measure of a time series. Three distinct time series classes were utilised to verify the feasibility of differentiating deterministic time series: deterministic, deterministic with noise, and stochastic. The LSTM model was constructed for each time series, and its features were thoroughly investigated. The findings of this study demonstrate a strong correlation between the root mean square error (RMSE) of the trained models and the determinism of a time series.

Suggested Citation

  • Janusz Miśkiewicz & Paweł Witkowicz, 2025. "Time Series Determinism Recognition by LSTM Model," Mathematics, MDPI, vol. 13(12), pages 1-13, June.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:12:p:2000-:d:1680999
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