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Permutations uniquely identify states and unknown external forces in non-autonomous dynamical systems
Yoshito Hirata1, Yuzuru Sato2, Davide Faranda3
1Mathematics and Informatics Center/Graduate School of Information Science and Technology/International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.
Abstract:
It has been shown that a permutation can uniquely identify the joint set of an initial condition and a non-autonomous external force realization added to the deterministic system in given time series data. We demonstrate that our results can be applied to time series forecasting as well as the estimation of common external forces. Thus, permutations provide a convenient description for a time series data set generated by non-autonomous dynamical systems.
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