81 citations · 139 across the 7 of their papers we have counts for
7 papers
Controlling Dynamical Systems into Unseen Target States Using Machine Learning
Daniel Köglmayr, Alexander Haluszczynski, Christoph Räth
We present a novel, model-free, and data-driven methodology for controlling complex dynamical systems into previously unseen target states, including those with significantly diffe…
Linear and nonlinear causality in financial markets
Haochun Ma, Davide Prosperino, Alexander Haluszczynski +1
Identifying and quantifying co-dependence between financial instruments is a key challenge for researchers and practitioners in the financial industry. Linear measures such as the…
Controlling dynamical systems to complex target states using machine learning: next-generation vs. classical reservoir computing
Alexander Haluszczynski, Daniel Köglmayr, Christoph Räth
Controlling nonlinear dynamical systems using machine learning allows to not only drive systems into simple behavior like periodicity but also to more complex arbitrary dynamics. F…
Controlling nonlinear dynamical systems into arbitrary states using machine learning
Alexander Haluszczynski, Christoph Räth
We propose a novel and fully data driven control scheme which relies on machine learning (ML). Exploiting recently developed ML-based prediction capabilities of complex systems, we…
Reducing network size and improving prediction stability of reservoir computing
Alexander Haluszczynski, Jonas Aumeier, Joschka Herteux +1
Reservoir computing is a very promising approach for the prediction of complex nonlinear dynamical systems. Besides capturing the exact short-term trajectories of nonlinear systems…
Good and bad predictions: Assessing and improving the replication of chaotic attractors by means of reservoir computing
Alexander Haluszczynski, Christoph Räth
The prediction of complex nonlinear dynamical systems with the help of machine learning techniques has become increasingly popular. In particular, reservoir computing turned out to…
Linear and nonlinear market correlations: characterizing financial crises and portfolio optimization
Alexander Haluszczynski, Ingo Laut, Heike Modest +1
Pearson correlation and mutual information based complex networks of the day-to-day returns of US S&P500 stocks between 1985 and 2015 have been constructed in order to investigate…