Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Haluszczynski, Alexander"'
We present a novel, model-free, and data-driven methodology for controlling complex dynamical systems into previously unseen target states, including those with significantly different and complex dynamics. Leveraging a parameter-aware realization of
Externí odkaz:
http://arxiv.org/abs/2412.10251
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 Pearson correlation are still widely used today, although their limite
Externí odkaz:
http://arxiv.org/abs/2312.16185
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. For this, it is crucial that a machine learning system can be trained t
Externí odkaz:
http://arxiv.org/abs/2307.07195
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 demonstrate that nonlinear systems can be forced to stay in arbitrary
Externí odkaz:
http://arxiv.org/abs/2102.12969
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, it has also proved to reproduce its characteristic long-term propert
Externí odkaz:
http://arxiv.org/abs/2003.03178
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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 be a very promising approach especially for the reproduction of the l
Externí odkaz:
http://arxiv.org/abs/1907.05639
Linear and nonlinear market correlations: characterizing financial crises and portfolio optimization
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 the mutual dependencies of the stocks and their nature. We show that b
Externí odkaz:
http://arxiv.org/abs/1712.02661
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Akademický článek
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