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Citez

Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: journal of the Econometric Society 424–438. https://doi.org/10.2307/1912791. Search in Google Scholar

Paluš, M., Krakovská, A., Jakubík, J., Chvosteková, M. (2018). Causality, dynamical systems and the arrow of time. Chaos: An Interdisciplinary Journal of Nonlinear Science 28(7), 075307. https://doi.org/10.1063/1.5019944. Search in Google Scholar

Haufe, S., Nikulin, V. V., Müller, K.-R., Nolte, G. (2013). A critical assessment of connectivity measures for eeg data: a simulation study. Neuroimage 64, 120–133. https://doi.org/10.1016/j.neuroimage.2012.09.036. Search in Google Scholar

Winkler, I., Panknin, D., Bartz, D., Müller, K.-R., Haufe, S. (2016). Validity of time reversal for testing granger causality. IEEE Transactions on Signal Processing 64(11), 2746–2760. https://doi.org/10.1109/TSP.2016.2531628. Search in Google Scholar

Riek, R. (2020). Entropy derived from causality. Entropy 22(6), 647. https://doi.org/10.3390/e22060647. Search in Google Scholar

Chvosteková, M., Jakubík, J., Krakovská, A. (2021). Granger causality on forward and reversed time series. Entropy 23(4), 409. https://doi.org/10.3390/e23040409. Search in Google Scholar

Kořenek, J., Hlinka, J. (2021). Causality in reversed time series: Reversed or conserved? Entropy 23(8), 1067. https://doi.org/10.3390/e23081067. Search in Google Scholar

Marinazzo, D., Pellicoro, M., Stramaglia, S. (2008). Kernel method for nonlinear granger causality. Physical review letters 100(14), 144103. https://doi.org/10.1103/PhysRevLett.100.144103. Search in Google Scholar

Krakovská, A., Hanzely, F. (2016). Testing for causality in reconstructed state spaces by an optimized mixed prediction method. Physical Review E 94(5), 052203. https://doi.org/10.1103/PhysRevE.94.052203. Search in Google Scholar

Chicharro, D., Andrzejak, R. G. (2009). Reliable detection of directional couplings using rank statistics. Physical Review E 80(2), 026217. https://doi.org/10.1103/PhysRevE.80.026217. Search in Google Scholar

Sugihara, G., May, R., Ye, H., Hsieh, C.-h., Deyle, E., Fogarty, M., Munch, S. (2012). Detecting causality in complex ecosystems. science 338(6106), 496–500. https://doi.org/10.1126/science.1227079 . Search in Google Scholar

Krakovská, A., Jakubík, J. (2020). Implementation of two causal methods based on predictions in reconstructed state spaces. Physical Review E 102(2), 022203. https://doi.org/10.1103/PhysRevE.102.022203. Search in Google Scholar

Walker, J. (2019). Tutorial: Time series analysis with pandas. https://www.dataquest.io/blog/tutorial-time-series-analysis-with-pandas/. Search in Google Scholar

Thorndike, R. L. (1953). Who belongs in the family. In Psychometrika. https://doi.org/10.1007/BF02289263. Search in Google Scholar

Takens, F. (1981). Detecting strange attractors in turbulence. In Dynamical systems and turbulence, Warwick 1980. Springer, 366–381. Search in Google Scholar

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Anglais
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Sujets de la revue:
Engineering, Electrical Engineering, Control Engineering, Metrology and Testing