ON THE USE OF A COMPLEX INDICATOR OF THE STABILITY OF PERMUTATION ENTROPY OF TIME SERIES FRAGMENTS WHEN ANALYZING INFRASOUND MONITORING SIGNALS OF THE ALTAI REPUBLIC

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N. Kudryavtsev

Gorno-Altaisk State University

编辑信件的主要联系方式.
Email: ngkudr@mail.ru
ORCID iD: 0000-0003-1327-5188
candidate of technical sciences 1996

I. Frolov

Gorno-Altaisk State University

Email: ngkudr@mail.ru
ORCID iD: 0000-0001-9176-6965

V. Safonova

Gorno-Altaisk State University

Email: ngkudr@mail.ru
ORCID iD: 0000-0002-8043-4014

参考

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  3. Higuchi, T. (1988), Approach to an irregular time series on the basis of the fractal theory, Physica D: Nonlinear Phenomena, 31(2), 277–283, https://doi.org/10.1016/0167-2789(88)90081-4.
  4. Kandal, M. (1981), Time series, Finance and Statistics, Moscow (in Russian).
  5. Liang, T., G. Xie, D. Mi, W. Jiang, and G. Xu (2020), PM2.5 Concentration Forecasting Based on Data Preprocessing Strategy and LSTM Neural Network, International Journal of Machine Learning and Computing, 10(6), 729–734, https://doi.org/10.18178/ijmlc.2020.10.6.997
  6. Lu, P., L. Ye, M. Pei, Y. Zhao, B. Dai, and Z. Li (2022), Short-term wind power forecasting based on meteorological feature extraction and optimization strategy, Renewable Energy, 184, 642–661, https://doi.org/10.1016/j.renene.2021.11.072.
  7. Microsin.net (2020), INMP441: digital microphone with interface I2S, https://microsin.net/adminstuff/hardware/inmp441-i2s-omnidirectional-digital-microphone.html (in Russian), (visited on 18.02.2024).
  8. Roushangar, K., R. Ghasempour, V. S. O. Kirca, and M. C. Demirel (2021), Hybrid point and interval prediction approaches for drought modeling using ground-based and remote sensing data, Hydrology Research, 52(6), 1469–1489, https://doi.org/10.2166/nh.2021.028.
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  11. Silva, A. S. A., R. S. C. Menezes, O. A. Rosso, B. Stosic, and T. Stosic (2021), Complexity entropy-analysis of monthly rainfall time series in northeastern Brazil, Chaos, Solitons & Fractals, 143, https://doi.org/10.1016/j.chaos.2020.110623.
  12. Soloviev, A., V. Lesur, and D. Kudin (2018), On the feasibility of routine baseline improvement in processing of geomagnetic observatory data, Earth, Planets and Space, 70(1), https://doi.org/10.1186/s40623-018-0786-8.
  13. St-Louis, B. (Ed.) (2020), INTERMAGNET Technical Reference Manual, Version 5.0.0, INTERMAGNET Operations Committee and Executive Council, https://doi.org/10.48440/INTERMAGNET.2020.001.
  14. Traversaro, F., F. O. Redelico, M. R. Risk, A. C. Frery, and O. A. Rosso (2018), Bandt-Pompe symbolization dynamics for time series with tied values: A data-driven approach, Chaos: An Interdisciplinary Journal of Nonlinear Science, 28(7), https://doi.org/10.1063/1.5022021.
  15. Zhang, T., C. Cheng, and P. Gao (2019), Permutation Entropy-Based Analysis of Temperature Complexity Spatial-Temporal Variation and Its Driving Factors in China, Entropy, 21(10), 1001, https://doi.org/10.3390/e21101001.
  16. Zhu, G., J. Hunter, and Y. Jiang (2016), Improved Prediction of Dengue Outbreak Using the Delay Permutation Entropy, in 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), IEEE, https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2016.172.
  17. Zunino, L., M. C. Soriano, and O. A. Rosso (2012), Distinguishing chaotic and stochastic dynamics from time series by using a multiscale symbolic approach, Physical Review E, 86(4), https://doi.org/10.1103/PhysRevE.86.046210.

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