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Physical Review. E|January 20, 2018
Linear and nonlinear market correlations: Characterizing financial crises and portfolio optimizationAlexander Haluszczynski, Ingo Laut, Heike Modest, et al.Chaos (Woodbury, N.Y.)|November 3, 2016
Surrogate-assisted network analysis of nonlinear time seriesIngo Laut, Christoph RäthScientific Reports|June 22, 2021
Controlling nonlinear dynamical systems into arbitrary states using machine learningAlexander Haluszczynski, Christoph RäthChaos (Woodbury, N.Y.)|November 3, 2019
Good and bad predictions: Assessing and improving the replication of chaotic attractors by means of reservoir computingAlexander Haluszczynski, Christoph RäthChaos (Woodbury, N.Y.)|November 1, 2022
Identifying causality drivers and deriving governing equations of nonlinear complex systemsHaochun Ma, Alexander Haluszczynski, Davide Prosperino, et al.Chaos (Woodbury, N.Y.)|July 3, 2020
Reducing network size and improving prediction stability of reservoir computingAlexander Haluszczynski, Jonas Aumeier, Joschka Herteux, et al.Chaos (Woodbury, N.Y.)|November 12, 2024
Linear and nonlinear causality in financial marketsHaochun Ma, Davide Prosperino, Alexander Haluszczynski, et al.Chaos (Woodbury, N.Y.)|June 12, 2023
Efficient forecasting of chaotic systems with block-diagonal and binary reservoir computingHaochun Ma, Davide Prosperino, Alexander Haluszczynski, et al.Scientific Reports|January 4, 2024
Extrapolating tipping points and simulating non-stationary dynamics of complex systems using efficient machine learningDaniel Köglmayr, Christoph RäthChaos (Woodbury, N.Y.)|October 13, 2023
Optimizing the combination of data-driven and model-based elements in hybrid reservoir computingDennis Duncan, Christoph RäthPageof 3