Empirical dynamic modeling (EDM) is a framework for analysis and prediction of nonlinear dynamical systems. Applications include population dynamics,[1][2][3][4][5][6] ecosystem service,[7] medicine,[8] neuroscience,[9][10][11] dynamical systems,[12][13][14] geophysics,[15][16][17] and human-computer interaction.[18] EDM was originally developed by Robert May and George Sugihara. It can be considered a methodology for data modeling, predictive analytics, dynamical system analysis, machine learning and time series analysis.
^[1]Dixon, P. A., et al. 1999. Episodic fluctuations in larval supply. Science 283:1528–1530
^[2]Hao Ye, Richard J. Beamish, Sarah M. Glaser, et al. 2015. Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling. Proceedings of the National Academy of Sciences Mar 2015, 112 (13) E1569-E1576; DOI: 10.1073/pnas.1417063112
^[3]Ethan R. Deyle, Michael Fogarty, Chih-hao Hsieh, et al. 2013. Proceedings of the National Academy of Sciences Apr 2013, 110 (16) 6430-6435; DOI: 10.1073/pnas.1215506110
^[4]Ushio, M., Hsieh, Ch., Masuda, R. et al., 2018. Fluctuating interaction network and time-varying stability of a natural fish community. Nature 554, 360–363
^[5]Deyle E.R., et al. 2016. Tracking and forecasting ecosystem interactions in real time. Proc. R. Soc. B 283: 20152258
^[6]Tanya L. Rogers, Stephan B. Munch, Simon D. Stewart, Eric P. Palkovacs, Alfredo Giron-Nava, Shin-ichiro S. Matsuzaki, Celia C. Symons. Ecology Letters, 23 (8) August 2020, 1287-1297
^[7]Park J., et al. 2021. Dynamics of Florida milk production and total phosphate in Lake Okeechobee. PLoS ONE 16(8): e0248910. doi:10.1371/journal.pone.0248910
^[8]George Sugihara, Walter Allan, Daniel Sobel, and Kenneth D. Allan, 1996. Nonlinear control of heart rate variability in human infants. Proc. Natl. Acad. Sci. USA. Vol. 93, pp. 2608-2613, March 1996. Medical Sciences
^[9]McBride, J. C., et al. Sugihara causality analysis of scalp EEG for detection of early Alzheimer's disease. Neuroimage-Clinical 7:258–265 (2015)
^[10]Tajima S, Yanagawa T, Fujii N, Toyoizumi T (2015) Untangling Brain-Wide Dynamics in Consciousness by Cross-Embedding. PLoS Comput Biol 11(11): e1004537. https://doi.org/10.1371/journal.pcbi.1004537
^[11]W. Watanakeesuntorn et al., "Massively Parallel Causal Inference of Whole Brain Dynamics at Single Neuron Resolution," 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS), 2020, pp. 196-205, doi: 10.1109/ICPADS51040.2020.00035
^[12] Deyle ER, Sugihara G (2011) Generalized Theorems for Nonlinear State Space Reconstruction. PLoS ONE 6(3): e18295. doi:10.1371/journal.pone.0018295
^[13]Ye, H., Deyle, E., Gilarranz, L. et al., 2015. Distinguishing time-delayed causal interactions using convergent cross mapping. Sci Rep 5, 14750 (2015). doi:10.1038/srep14750
^[14]Cenci, S., Saavedra, S. Non-parametric estimation of the structural stability of non-equilibrium community dynamics. Nat Ecol Evol 3, 912–918 (2019). https://doi.org/10.1038/s41559-019-0879-1
^[15]Tsonis A. A., et al. Dynamical evidence for causality between galactic cosmic rays and interannual variation in global temperature. Proc Natl Acad Sci 112(11):3253–3256 (2015).
^[16]Nes EH Van, et al. Causal feedbacks in climate change. Nat Clim Chang 5(5):445–448 (2015)
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