Articles | Volume 21, issue 6
https://doi.org/10.5194/npg-21-1085-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/npg-21-1085-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
11 Nov 2014
Research article |
| 11 Nov 2014
Finding recurrence networks' threshold adaptively for a specific time series
D. Eroglu
CORRESPONDING AUTHOR
Potsdam Institute for Climate Impact Research, Potsdam, Germany
Department of Physics, Humboldt University of Berlin, Berlin, Germany
N. Marwan
Potsdam Institute for Climate Impact Research, Potsdam, Germany
S. Prasad
Institute of Earth and Environmental Science, Potsdam University, Potsdam, Germany
J. Kurths
Potsdam Institute for Climate Impact Research, Potsdam, Germany
Department of Physics, Humboldt University of Berlin, Berlin, Germany
Institute of Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, UK
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Cited
65 citations as recorded by crossref.
- Multiplex recurrence networks D. Eroglu et al. https://doi.org/10.1103/PhysRevE.97.012312
- Coordinative patterns underlying cross-linguistic rhythmic differences L. Lancia et al. https://doi.org/10.1016/j.wocn.2018.08.004
- Complex network based techniques to identify extreme events and (sudden) transitions in spatio-temporal systems N. Marwan & J. Kurths https://doi.org/10.1063/1.4916924
- A Brief Introduction to Nonlinear Time Series Analysis and Recurrence Plots B. Goswami https://doi.org/10.3390/vibration2040021
- A pseudo-basis using a recurrence plot M. Shiro & Y. Hirata https://doi.org/10.1140/epjs/s11734-022-00702-7
- Soil moisture dynamics in global land areas: Insights from recurrence structures A. Prabhakar & B. Sivakumar https://doi.org/10.1016/j.chaos.2026.118771
- Complex network approaches to nonlinear time series analysis Y. Zou et al. https://doi.org/10.1016/j.physrep.2018.10.005
- A variable threshold for recurrence based on local attractor density R. Delage & T. Nakata https://doi.org/10.1063/5.0114797
- Optimizing self-organized topology of recurrence-based complex networks C. Li et al. https://doi.org/10.1063/5.0249500
- See–saw relationship of the Holocene East Asian–Australian summer monsoon D. Eroglu et al. https://doi.org/10.1038/ncomms12929
- Automatic detection of abrupt transitions in paleoclimate records W. Bagniewski et al. https://doi.org/10.1063/5.0062543
- Recurrence network analysis of schizophrenia MEG under different stimulation states D. Bai et al. https://doi.org/10.1016/j.bspc.2022.104310
- How to compute suitable vicinity parameter and sampling time of recurrence analysis T. de Lima Prado et al. https://doi.org/10.1007/s11071-023-09063-9
- Recurrence network analysis in a model tripartite quantum system P. Laha et al. https://doi.org/10.1209/0295-5075/125/60005
- The reliability of recurrence network analysis is influenced by the observability properties of the recorded time series L. Portes et al. https://doi.org/10.1063/1.5093197
- Recurrence network analysis exploring the routes to thermoacoustic instability in a Rijke tube with inverse diffusion flame A. Bhattacharya et al. https://doi.org/10.1063/5.0026943
- LSD flattens the hierarchy of directed information flow in fast whole-brain dynamics K. Shinozuka et al. https://doi.org/10.1162/imag_a_00420
- Study of system dynamics through recurrence analysis of regular windows A. Rysak & M. Gregorczyk https://doi.org/10.1063/5.0036505
- Cross over of recurrence networks to random graphs and random geometric graphs R. JACOB et al. https://doi.org/10.1007/s12043-016-1339-y
- Characteristics of Air Traffic Flow in Terminal Airspace: A Multiplex Recurrence Network Analysis F. Jiang & Z. Zhang https://doi.org/10.1109/TITS.2024.3396627
- Characterizing the nonlinear dynamics of hydrological systems based on global recurrence analysis S. Yu et al. https://doi.org/10.1016/j.jhydrol.2025.132817
- Lean blowout detection using topological data analysis A. Bhattacharya et al. https://doi.org/10.1063/5.0156500
- Network-based methodology to determine obstructive sleep apnea A. Vaysi et al. https://doi.org/10.1016/j.physa.2025.130714
- A novel methodology for emotion recognition through 62-lead EEG signals: multilevel heterogeneous recurrence analysis Y. Wang et al. https://doi.org/10.3389/fphys.2024.1425582
- A graph convolutional network for time series classification using recurrence plots H. Kang et al. https://doi.org/10.1007/s10489-025-06841-3
- Network correlation between investor’s herding behavior and overconfidence behavior* M. Zhang & Y. Wang https://doi.org/10.1088/1674-1056/ab7740
- A non-destructive dropped fruit impact signal imaging-based deep learning approach for smart sorting of kiwifruit Y. Yang et al. https://doi.org/10.1016/j.compag.2022.107380
- Characterizing the signature of flame flashback precursor through recurrence analysis L. Christodoulou et al. https://doi.org/10.1063/1.4940154
- Bifurcations, time-series analysis of observables, and network properties in a tripartite quantum system P. Laha et al. https://doi.org/10.1016/j.physleta.2020.126565
- Dispersion heterogeneous recurrence analysis and its use on fault detection B. Zhang et al. https://doi.org/10.1016/j.cnsns.2022.106902
- Reservoir computing approaches to unsupervised concept drift detection in dynamical systems B. Thorne et al. https://doi.org/10.1063/5.0234779
- Determining periodic orbits via nonlinear filtering and recurrence spectra in the presence of noise S. Oberst et al. https://doi.org/10.1016/j.proeng.2017.09.046
- Uniform framework for the recurrence-network analysis of chaotic time series R. Jacob et al. https://doi.org/10.1103/PhysRevE.93.012202
- Early detection of lean blowout in a combustor using symbolic analysis of colour images S. De et al. https://doi.org/10.1016/j.measurement.2021.110113
- A bottom-up approach for recurrence detection based on sampling distance R. Delage & T. Nakata https://doi.org/10.1063/5.0160832
- Detection of changes in the dynamics of thermonuclear plasmas to improve the prediction of disruptions T. Craciunescu & A. Murari https://doi.org/10.1007/s11071-022-08009-x
- Fractal analysis of recurrence networks constructed from the two-dimensional fractional Brownian motions J. Liu et al. https://doi.org/10.1063/5.0003884
- Characterization of chaotic attractors under noise: A recurrence network perspective R. Jacob et al. https://doi.org/10.1016/j.cnsns.2016.04.028
- Recurrence threshold selection for obtaining robust recurrence characteristics in different embedding dimensions K. Kraemer et al. https://doi.org/10.1063/1.5024914
- Detection of unstable periodic orbits in mineralising geological systems S. Oberst et al. https://doi.org/10.1063/1.5024134
- On a topological criterion to select a recurrence threshold I. Andreadis et al. https://doi.org/10.1063/1.5116766
- Building functional networks for complex response analysis in systems of coupled nonlinear oscillators C. Geier et al. https://doi.org/10.1016/j.jsv.2024.118544
- Transformation cost spectrum for irregularly sampled time series C. Ozdes & D. Eroglu https://doi.org/10.1140/epjs/s11734-022-00512-x
- Recurrence plot analysis of irregularly sampled data I. Ozken et al. https://doi.org/10.1103/PhysRevE.98.052215
- Application of recurrence quantification analysis for early detection of lean blowout in a swirl-stabilized dump combustor S. De et al. https://doi.org/10.1063/1.5131231
- Network structure entropy and its dynamical evolution for recurrence networks from earthquake magnitude time series M. Lin et al. https://doi.org/10.1140/epjb/e2016-70004-0
- Analyzing long-term correlated stochastic processes by means of recurrence networks: Potentials and pitfalls Y. Zou et al. https://doi.org/10.1103/PhysRevE.91.022926
- Finding metastable states in real-world time series with recurrence networks I. Vega et al. https://doi.org/10.1016/j.physa.2015.10.041
- An Automatic Process Monitoring Method Using Recurrence Plot in Progressive Stamping Processes C. Zhou et al. https://doi.org/10.1109/TASE.2015.2468058
- A Recurrence Network Approach for Characterization and Detection of Dynamical Transitions During Human Speech Production G. Lal et al. https://doi.org/10.1007/s00034-022-02103-6
- Early detection of lean blowout using recurrence network for varying degrees of premixedness A. Bhattacharya et al. https://doi.org/10.1063/5.0077436
- Directed recurrence networks R. Delage & T. Nakata https://doi.org/10.1063/5.0173394
- Time-Series and Network Analysis in Quantum Dynamics: Comparison with Classical Dynamics P. Laha et al. https://doi.org/10.1007/s10773-020-04610-1
- Data-driven stability analysis in a multi-element supercritical liquid oxygen–methane combustor A. Bhattacharya et al. https://doi.org/10.1063/5.0263601
- Non-linear regime shifts in Holocene Asian monsoon variability: potential impacts on cultural change and migratory patterns J. Donges et al. https://doi.org/10.5194/cp-11-709-2015
- Markov modeling via ordinal partitions: An alternative paradigm for network-based time-series analysis K. Sakellariou et al. https://doi.org/10.1103/PhysRevE.100.062307
- Complex Network Methods for Plastic Deformation Dynamics in Metals A. Kiv et al. https://doi.org/10.3390/dynamics3010004
- Trends in recurrence analysis of dynamical systems N. Marwan & K. Kraemer https://doi.org/10.1140/epjs/s11734-022-00739-8
- Transformation-cost time-series method for analyzing irregularly sampled data I. Ozken et al. https://doi.org/10.1103/PhysRevE.91.062911
- Quantum-inspired density-matrix recurrence analysis of brain time series M. Mannone & N. Marwan https://doi.org/10.1140/epjs/s11734-026-02340-9
- Mapping and discrimination of networks in the complexity-entropy plane M. Wiedermann et al. https://doi.org/10.1103/PhysRevE.96.042304
- Uncovering complexity details in actigraphy patterns to differentiate the depressed from the non-depressed S. George et al. https://doi.org/10.1038/s41598-021-92890-w
- Recurrence network modeling and analysis of spatial data C. Chen et al. https://doi.org/10.1063/1.5024917
- Recurrence quantification analysis for binary neutron star coalescence simulations G. Conde-Saavedra et al. https://doi.org/10.1140/epjs/s11734-026-02538-x
- Deep RP-CNN for Burst Signal Detection in Cognitive Radios D. Seo & H. Nam https://doi.org/10.1109/ACCESS.2020.3023262
65 citations as recorded by crossref.
- Multiplex recurrence networks D. Eroglu et al. https://doi.org/10.1103/PhysRevE.97.012312
- Coordinative patterns underlying cross-linguistic rhythmic differences L. Lancia et al. https://doi.org/10.1016/j.wocn.2018.08.004
- Complex network based techniques to identify extreme events and (sudden) transitions in spatio-temporal systems N. Marwan & J. Kurths https://doi.org/10.1063/1.4916924
- A Brief Introduction to Nonlinear Time Series Analysis and Recurrence Plots B. Goswami https://doi.org/10.3390/vibration2040021
- A pseudo-basis using a recurrence plot M. Shiro & Y. Hirata https://doi.org/10.1140/epjs/s11734-022-00702-7
- Soil moisture dynamics in global land areas: Insights from recurrence structures A. Prabhakar & B. Sivakumar https://doi.org/10.1016/j.chaos.2026.118771
- Complex network approaches to nonlinear time series analysis Y. Zou et al. https://doi.org/10.1016/j.physrep.2018.10.005
- A variable threshold for recurrence based on local attractor density R. Delage & T. Nakata https://doi.org/10.1063/5.0114797
- Optimizing self-organized topology of recurrence-based complex networks C. Li et al. https://doi.org/10.1063/5.0249500
- See–saw relationship of the Holocene East Asian–Australian summer monsoon D. Eroglu et al. https://doi.org/10.1038/ncomms12929
- Automatic detection of abrupt transitions in paleoclimate records W. Bagniewski et al. https://doi.org/10.1063/5.0062543
- Recurrence network analysis of schizophrenia MEG under different stimulation states D. Bai et al. https://doi.org/10.1016/j.bspc.2022.104310
- How to compute suitable vicinity parameter and sampling time of recurrence analysis T. de Lima Prado et al. https://doi.org/10.1007/s11071-023-09063-9
- Recurrence network analysis in a model tripartite quantum system P. Laha et al. https://doi.org/10.1209/0295-5075/125/60005
- The reliability of recurrence network analysis is influenced by the observability properties of the recorded time series L. Portes et al. https://doi.org/10.1063/1.5093197
- Recurrence network analysis exploring the routes to thermoacoustic instability in a Rijke tube with inverse diffusion flame A. Bhattacharya et al. https://doi.org/10.1063/5.0026943
- LSD flattens the hierarchy of directed information flow in fast whole-brain dynamics K. Shinozuka et al. https://doi.org/10.1162/imag_a_00420
- Study of system dynamics through recurrence analysis of regular windows A. Rysak & M. Gregorczyk https://doi.org/10.1063/5.0036505
- Cross over of recurrence networks to random graphs and random geometric graphs R. JACOB et al. https://doi.org/10.1007/s12043-016-1339-y
- Characteristics of Air Traffic Flow in Terminal Airspace: A Multiplex Recurrence Network Analysis F. Jiang & Z. Zhang https://doi.org/10.1109/TITS.2024.3396627
- Characterizing the nonlinear dynamics of hydrological systems based on global recurrence analysis S. Yu et al. https://doi.org/10.1016/j.jhydrol.2025.132817
- Lean blowout detection using topological data analysis A. Bhattacharya et al. https://doi.org/10.1063/5.0156500
- Network-based methodology to determine obstructive sleep apnea A. Vaysi et al. https://doi.org/10.1016/j.physa.2025.130714
- A novel methodology for emotion recognition through 62-lead EEG signals: multilevel heterogeneous recurrence analysis Y. Wang et al. https://doi.org/10.3389/fphys.2024.1425582
- A graph convolutional network for time series classification using recurrence plots H. Kang et al. https://doi.org/10.1007/s10489-025-06841-3
- Network correlation between investor’s herding behavior and overconfidence behavior* M. Zhang & Y. Wang https://doi.org/10.1088/1674-1056/ab7740
- A non-destructive dropped fruit impact signal imaging-based deep learning approach for smart sorting of kiwifruit Y. Yang et al. https://doi.org/10.1016/j.compag.2022.107380
- Characterizing the signature of flame flashback precursor through recurrence analysis L. Christodoulou et al. https://doi.org/10.1063/1.4940154
- Bifurcations, time-series analysis of observables, and network properties in a tripartite quantum system P. Laha et al. https://doi.org/10.1016/j.physleta.2020.126565
- Dispersion heterogeneous recurrence analysis and its use on fault detection B. Zhang et al. https://doi.org/10.1016/j.cnsns.2022.106902
- Reservoir computing approaches to unsupervised concept drift detection in dynamical systems B. Thorne et al. https://doi.org/10.1063/5.0234779
- Determining periodic orbits via nonlinear filtering and recurrence spectra in the presence of noise S. Oberst et al. https://doi.org/10.1016/j.proeng.2017.09.046
- Uniform framework for the recurrence-network analysis of chaotic time series R. Jacob et al. https://doi.org/10.1103/PhysRevE.93.012202
- Early detection of lean blowout in a combustor using symbolic analysis of colour images S. De et al. https://doi.org/10.1016/j.measurement.2021.110113
- A bottom-up approach for recurrence detection based on sampling distance R. Delage & T. Nakata https://doi.org/10.1063/5.0160832
- Detection of changes in the dynamics of thermonuclear plasmas to improve the prediction of disruptions T. Craciunescu & A. Murari https://doi.org/10.1007/s11071-022-08009-x
- Fractal analysis of recurrence networks constructed from the two-dimensional fractional Brownian motions J. Liu et al. https://doi.org/10.1063/5.0003884
- Characterization of chaotic attractors under noise: A recurrence network perspective R. Jacob et al. https://doi.org/10.1016/j.cnsns.2016.04.028
- Recurrence threshold selection for obtaining robust recurrence characteristics in different embedding dimensions K. Kraemer et al. https://doi.org/10.1063/1.5024914
- Detection of unstable periodic orbits in mineralising geological systems S. Oberst et al. https://doi.org/10.1063/1.5024134
- On a topological criterion to select a recurrence threshold I. Andreadis et al. https://doi.org/10.1063/1.5116766
- Building functional networks for complex response analysis in systems of coupled nonlinear oscillators C. Geier et al. https://doi.org/10.1016/j.jsv.2024.118544
- Transformation cost spectrum for irregularly sampled time series C. Ozdes & D. Eroglu https://doi.org/10.1140/epjs/s11734-022-00512-x
- Recurrence plot analysis of irregularly sampled data I. Ozken et al. https://doi.org/10.1103/PhysRevE.98.052215
- Application of recurrence quantification analysis for early detection of lean blowout in a swirl-stabilized dump combustor S. De et al. https://doi.org/10.1063/1.5131231
- Network structure entropy and its dynamical evolution for recurrence networks from earthquake magnitude time series M. Lin et al. https://doi.org/10.1140/epjb/e2016-70004-0
- Analyzing long-term correlated stochastic processes by means of recurrence networks: Potentials and pitfalls Y. Zou et al. https://doi.org/10.1103/PhysRevE.91.022926
- Finding metastable states in real-world time series with recurrence networks I. Vega et al. https://doi.org/10.1016/j.physa.2015.10.041
- An Automatic Process Monitoring Method Using Recurrence Plot in Progressive Stamping Processes C. Zhou et al. https://doi.org/10.1109/TASE.2015.2468058
- A Recurrence Network Approach for Characterization and Detection of Dynamical Transitions During Human Speech Production G. Lal et al. https://doi.org/10.1007/s00034-022-02103-6
- Early detection of lean blowout using recurrence network for varying degrees of premixedness A. Bhattacharya et al. https://doi.org/10.1063/5.0077436
- Directed recurrence networks R. Delage & T. Nakata https://doi.org/10.1063/5.0173394
- Time-Series and Network Analysis in Quantum Dynamics: Comparison with Classical Dynamics P. Laha et al. https://doi.org/10.1007/s10773-020-04610-1
- Data-driven stability analysis in a multi-element supercritical liquid oxygen–methane combustor A. Bhattacharya et al. https://doi.org/10.1063/5.0263601
- Non-linear regime shifts in Holocene Asian monsoon variability: potential impacts on cultural change and migratory patterns J. Donges et al. https://doi.org/10.5194/cp-11-709-2015
- Markov modeling via ordinal partitions: An alternative paradigm for network-based time-series analysis K. Sakellariou et al. https://doi.org/10.1103/PhysRevE.100.062307
- Complex Network Methods for Plastic Deformation Dynamics in Metals A. Kiv et al. https://doi.org/10.3390/dynamics3010004
- Trends in recurrence analysis of dynamical systems N. Marwan & K. Kraemer https://doi.org/10.1140/epjs/s11734-022-00739-8
- Transformation-cost time-series method for analyzing irregularly sampled data I. Ozken et al. https://doi.org/10.1103/PhysRevE.91.062911
- Quantum-inspired density-matrix recurrence analysis of brain time series M. Mannone & N. Marwan https://doi.org/10.1140/epjs/s11734-026-02340-9
- Mapping and discrimination of networks in the complexity-entropy plane M. Wiedermann et al. https://doi.org/10.1103/PhysRevE.96.042304
- Uncovering complexity details in actigraphy patterns to differentiate the depressed from the non-depressed S. George et al. https://doi.org/10.1038/s41598-021-92890-w
- Recurrence network modeling and analysis of spatial data C. Chen et al. https://doi.org/10.1063/1.5024917
- Recurrence quantification analysis for binary neutron star coalescence simulations G. Conde-Saavedra et al. https://doi.org/10.1140/epjs/s11734-026-02538-x
- Deep RP-CNN for Burst Signal Detection in Cognitive Radios D. Seo & H. Nam https://doi.org/10.1109/ACCESS.2020.3023262
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