Dokument-ID Dokumenttyp Verfasser/Autoren Herausgeber Haupttitel Abstract Auflage Verlagsort Verlag Erscheinungsjahr Seitenzahl Schriftenreihe Titel Schriftenreihe Bandzahl ISBN Quelle der Hochschulschrift Konferenzname Quelle:Titel Quelle:Jahrgang Quelle:Heftnummer Quelle:Erste Seite Quelle:Letzte Seite URN DOI Abteilungen
OPUS4-6253 Wissenschaftlicher Artikel Vega, Iliusi; Schütte, Christof; Conrad, Tim Finding metastable states in real-world time series with recurrence networks In the framework of time series analysis with recurrence networks, we introduce a self-adaptive method that determines the elusive recurrence threshold and identifies metastable states in complex real-world time series. As initial step, we introduce a way to set the embedding parameters used to reconstruct the state space from the time series. We set them as the ones giving the maximum Shannon entropy of the diagonal line length distribution for the first simultaneous minima of recurrence rate and Shannon entropy. To identify metastable states, as well as the transitions between them, we use a soft partitioning algorithm for module finding which is specifically developed for the case in which a system shows metastability. We illustrate our method with a complex time series example. Finally, we show the robustness of our method for identifying metastable states. Our results suggest that our method is robust for identifying metastable states in complex time series, even when introducing considerable levels of noise and missing data points. 2016 16 Physica A: Statistical Mechanics and its Applications 445 1 17 10.1016/j.physa.2015.10.041 Numerical Mathematics
OPUS4-6379 Dissertation Vega, Iliusi Reconstruction and analysis of the state space for the identification of dynamical states in real-world time-series 2016 urn:nbn:de:kobv:188-fudissthesis000000104520-1 Numerical Mathematics
OPUS4-5013 misc Vega, Iliusi; Schütte, Christof; Conrad, Tim SAIMeR: Self-adapted method for the identification of metastable states in real-world time series In the framework of time series analysis with recurrence networks, we introduce SAIMeR, a heuristic self-adapted method that determines the elusive recurrence threshold and identifies metastable states in complex time series. To identify metastable states as well as the transitions between them, we use graph theory concepts and a fuzzy partitioning clustering algorithm. We illustrate SAIMeR by applying it to three real-world time series and show that it is able to identify metastable states in real-world data with noise and missing data points. Finally, we suggest a way to choose the embedding parameters used to construct the state space in which this method is performed, based on the analysis of how the values of these parameters affect two recurrence quantitative measurements: recurrence rate and entropy. 2014 urn:nbn:de:0297-zib-50130 Numerical Mathematics