Network-based characterization of time series and its application to signal classification
Time series analysis in complex systems can help us to peep into the inner structure and operation law of the system so as to make relevant decisions. In this paper, we propose a binary symbolic pattern state transfer network for measuring the complexity of series. First, we capture the spatio-tempo...
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| Veröffentlicht in: | Chaos, solitons and fractals Jg. 201; S. 117300 |
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| Abstract | Time series analysis in complex systems can help us to peep into the inner structure and operation law of the system so as to make relevant decisions. In this paper, we propose a binary symbolic pattern state transfer network for measuring the complexity of series. First, we capture the spatio-temporal characteristics of series through a weighted change pattern matrix, and then we define a new binary coding mode that accomplishes the conversion of complex series to symbolic series. In addition, we fully consider the temporal evolution of the series, construct a horizontal viewable view of the state transfer series and generate a complex network, and extract the relevant metrics. Simulation experiments verify the validity of the model and its robustness to parameters. Finally, the model is applied to physiological signal analysis. For two EEG datasets, we depicted the brain region activities of the subjects in different states and successfully categorized the subjects. In summary, our approach captures the intrinsic patterns and features of series from a new perspective and provides an effective way to measure the complexity of series, it also provides an effective way to recognize and classify complex signals.
•A new metric for measuring the complexity of time series is proposed.•The global properties of complex series are comprehensively considered.•A new binary coding method is proposed to encode sequences symbolically.•Considers the evolution of sequence states over time and construct the network.•The constructed metrics are can classify real-world data effectively. |
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| AbstractList | Time series analysis in complex systems can help us to peep into the inner structure and operation law of the system so as to make relevant decisions. In this paper, we propose a binary symbolic pattern state transfer network for measuring the complexity of series. First, we capture the spatio-temporal characteristics of series through a weighted change pattern matrix, and then we define a new binary coding mode that accomplishes the conversion of complex series to symbolic series. In addition, we fully consider the temporal evolution of the series, construct a horizontal viewable view of the state transfer series and generate a complex network, and extract the relevant metrics. Simulation experiments verify the validity of the model and its robustness to parameters. Finally, the model is applied to physiological signal analysis. For two EEG datasets, we depicted the brain region activities of the subjects in different states and successfully categorized the subjects. In summary, our approach captures the intrinsic patterns and features of series from a new perspective and provides an effective way to measure the complexity of series, it also provides an effective way to recognize and classify complex signals.
•A new metric for measuring the complexity of time series is proposed.•The global properties of complex series are comprehensively considered.•A new binary coding method is proposed to encode sequences symbolically.•Considers the evolution of sequence states over time and construct the network.•The constructed metrics are can classify real-world data effectively. |
| ArticleNumber | 117300 |
| Author | Mi, Yujia Lin, Aijing |
| Author_xml | – sequence: 1 givenname: Yujia surname: Mi fullname: Mi, Yujia email: 23111518@bjtu.edu.cn – sequence: 2 givenname: Aijing orcidid: 0000-0002-1088-7296 surname: Lin fullname: Lin, Aijing email: ajlin@bjtu.edu.cn |
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| Cites_doi | 10.1016/j.physleta.2004.07.066 10.23919/JCC.ea.2022-0180.202401 10.1016/j.eswa.2022.119227 10.1103/PhysRevE.80.046103 10.1103/PhysRevE.72.046220 10.1007/s11071-021-07059-x 10.1016/S0076-6879(04)84011-4 10.1016/j.physrep.2006.11.001 10.1063/1.166092 10.1016/j.chaos.2023.114436 10.1016/j.patcog.2023.110075 10.1063/5.0142230 10.1063/5.0048243 10.1016/j.patcog.2023.110121 10.1098/rsta.2016.0292 10.1007/s11071-023-08776-1 10.1016/j.jmsy.2020.05.004 10.1016/j.patcog.2024.110486 10.1016/j.bspc.2020.101951 10.1016/j.patcog.2009.08.015 10.1103/PhysRevLett.88.174102 10.1103/PhysRevE.71.021906 10.1109/LSP.2015.2482603 10.1063/1.5086527 10.1109/TBME.2017.2664105 10.1016/j.ymssp.2020.107182 10.1109/TIT.2022.3143764 10.1016/j.compbiomed.2021.105120 10.3390/e21060541 10.1073/pnas.0709247105 10.1016/j.physa.2013.07.075 10.1038/srep35622 10.1109/JIOT.2024.3440332 |
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| Keywords | Horizontal visibility graph Signal recognition and classification Time series complexity Phase space reconstruction |
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| References | Li, Wang, Deng (b1) 2021; 61 Romano, Thiel, Kurths, von Bloh (b22) 2004; 330 Groth (b21) 2005; 72 McCullough, Small, Iu, Stemler (b25) 2017; 375 Li, Jiao, Zhu, Li (b32) 2023; 111 Baldán, Benítez (b31) 2023; 213 Ruan, Donner, Guan, Zou (b26) 2019; 29 Xiaochuan, Difei, Biao, Zhigang, Yingqi (b24) 2024 Delgado-Bonal, Marshak (b6) 2019; 21 Bandt, Pompe (b7) 2002; 88 Chen, Han, Chen, Guo (b2) 2017; 7 Costa, Goldberger, Peng (b9) 2005; 71 Wu, Wu, Lee, Lin (b10) 2013; 392 Younis, Hakmeh, Ahmadi (b34) 2024; 152 Motie Nasrabadi, Allahverdy, Samavati, Mohammadi (b35) 2020 Alakus, Gonen, Turkoglu (b36) 2020; 60 Digulescu, Murgan, Ioana, Candel, Serbanescu (b23) 2016 Richman, Lake, Moorman (b5) 2004; Vol. 384 Lacasa, Luque, Ballesteros, Luque, Nuno (b16) 2008; 105 Mbouopda, Mephu Nguifo (b33) 2024; 147 Mazuelas, Shen, Pérez (b14) 2022; 68 Cardoso-Pereira, Borges, Barros, Loureiro, Rosso, Ramos (b29) 2022; 107 Pincus (b4) 1995; 5 Hao, Sun, Li, Huang (b27) 2024 Humeau-Heurtier, Wu, Wu (b13) 2015; 22 Iaconis, Jiménez, Gasaneo, Rosso, Delrieux (b28) 2023; 33 Xuan, Zhou, Qiu, Xu, Zheng, Yang (b20) 2022; 32 Sun, Li, Song, Cai, Zhang, Hong (b3) 2024; 147 Marwan, Romano, Thiel, Kurths (b17) 2007; 438 Li, Tang, Jiao, Zhou (b11) 2024; 179 Gao, Cai, Yang, Dang, Zhang (b19) 2016; 6 Koh, Ooi, Lim-Ashworth, Vicnesh, Tor, Lih, Tan, Acharya, Fung (b15) 2022; 140 Pham (b30) 2010; 43 Manis, Aktaruzzaman, Sassi (b8) 2017; 64 Luque, Lacasa, Ballesteros, Luque (b18) 2009; 80 Yang, Jia (b12) 2021; 149 Koh (10.1016/j.chaos.2025.117300_b15) 2022; 140 Manis (10.1016/j.chaos.2025.117300_b8) 2017; 64 Cardoso-Pereira (10.1016/j.chaos.2025.117300_b29) 2022; 107 Pham (10.1016/j.chaos.2025.117300_b30) 2010; 43 Yang (10.1016/j.chaos.2025.117300_b12) 2021; 149 Lacasa (10.1016/j.chaos.2025.117300_b16) 2008; 105 McCullough (10.1016/j.chaos.2025.117300_b25) 2017; 375 Gao (10.1016/j.chaos.2025.117300_b19) 2016; 6 Delgado-Bonal (10.1016/j.chaos.2025.117300_b6) 2019; 21 Mazuelas (10.1016/j.chaos.2025.117300_b14) 2022; 68 Xuan (10.1016/j.chaos.2025.117300_b20) 2022; 32 Baldán (10.1016/j.chaos.2025.117300_b31) 2023; 213 Iaconis (10.1016/j.chaos.2025.117300_b28) 2023; 33 Younis (10.1016/j.chaos.2025.117300_b34) 2024; 152 Costa (10.1016/j.chaos.2025.117300_b9) 2005; 71 Motie Nasrabadi (10.1016/j.chaos.2025.117300_b35) 2020 Pincus (10.1016/j.chaos.2025.117300_b4) 1995; 5 Alakus (10.1016/j.chaos.2025.117300_b36) 2020; 60 Richman (10.1016/j.chaos.2025.117300_b5) 2004; Vol. 384 Chen (10.1016/j.chaos.2025.117300_b2) 2017; 7 Ruan (10.1016/j.chaos.2025.117300_b26) 2019; 29 Xiaochuan (10.1016/j.chaos.2025.117300_b24) 2024 Groth (10.1016/j.chaos.2025.117300_b21) 2005; 72 Romano (10.1016/j.chaos.2025.117300_b22) 2004; 330 Humeau-Heurtier (10.1016/j.chaos.2025.117300_b13) 2015; 22 Digulescu (10.1016/j.chaos.2025.117300_b23) 2016 Bandt (10.1016/j.chaos.2025.117300_b7) 2002; 88 Wu (10.1016/j.chaos.2025.117300_b10) 2013; 392 Li (10.1016/j.chaos.2025.117300_b32) 2023; 111 Mbouopda (10.1016/j.chaos.2025.117300_b33) 2024; 147 Sun (10.1016/j.chaos.2025.117300_b3) 2024; 147 Luque (10.1016/j.chaos.2025.117300_b18) 2009; 80 Li (10.1016/j.chaos.2025.117300_b1) 2021; 61 Li (10.1016/j.chaos.2025.117300_b11) 2024; 179 Marwan (10.1016/j.chaos.2025.117300_b17) 2007; 438 Hao (10.1016/j.chaos.2025.117300_b27) 2024 |
| References_xml | – volume: 80 year: 2009 ident: b18 article-title: Horizontal visibility graphs: Exact results for random time series publication-title: Phys Rev E – volume: 147 year: 2024 ident: b33 article-title: Scalable and accurate subsequence transform for time series classification publication-title: Pattern Recognit – start-page: 19 year: 2016 end-page: 38 ident: b23 article-title: Applications of transient signal analysis using the concept of recurrence plot analysis publication-title: Recurrence plots and their quantifications: expanding horizons: proceedings of the 6th international symposium on recurrence plots, Grenoble, France, 17-19 June 2015 – volume: 33 year: 2023 ident: b28 article-title: Ordinal pattern transition networks in eye tracking reading signals publication-title: Chaos: An Interdiscip J Nonlinear Sci – volume: 43 start-page: 887 year: 2010 end-page: 896 ident: b30 article-title: GeoEntropy: A measure of complexity and similarity publication-title: Pattern Recognit – year: 2020 ident: b35 article-title: EEG data for ADHD / control children – volume: 21 start-page: 541 year: 2019 ident: b6 article-title: Approximate entropy and sample entropy: A comprehensive tutorial publication-title: Entropy – year: 2024 ident: b27 article-title: Detecting tendency behavior of cellular network traffic via ordinal pattern transition networks publication-title: IEEE Internet Things J – volume: 152 year: 2024 ident: b34 article-title: MTS2graph: Interpretable multivariate time series classification with temporal evolving graphs publication-title: Pattern Recognit – volume: 68 start-page: 2530 year: 2022 end-page: 2550 ident: b14 article-title: Generalized maximum entropy for supervised classification publication-title: IEEE Trans Inform Theory – volume: 140 year: 2022 ident: b15 article-title: Automated classification of attention deficit hyperactivity disorder and conduct disorder using entropy features with ECG signals publication-title: Comput Biol Med – volume: 375 year: 2017 ident: b25 article-title: Multiscale ordinal network analysis of human cardiac dynamics publication-title: Philos Trans R Soc A: Math, Phys Eng Sci – volume: Vol. 384 start-page: 172 year: 2004 end-page: 184 ident: b5 article-title: Sample entropy publication-title: Methods in enzymology – volume: 107 start-page: 889 year: 2022 end-page: 908 ident: b29 article-title: Leveraging the self-transition probability of ordinal patterns transition network for transportation mode identification based on GPS data publication-title: Nonlinear Dynam – volume: 392 start-page: 5865 year: 2013 end-page: 5873 ident: b10 article-title: Modified multiscale entropy for short-term time series analysis publication-title: Phys A – volume: 88 year: 2002 ident: b7 article-title: Permutation entropy: a natural complexity measure for time series publication-title: Phys Rev Lett – volume: 32 year: 2022 ident: b20 article-title: CLPVG: Circular limited penetrable visibility graph as a new network model for time series publication-title: Chaos: An Interdiscip J Nonlinear Sci – volume: 64 start-page: 2711 year: 2017 end-page: 2718 ident: b8 article-title: Bubble entropy: An entropy almost free of parameters publication-title: IEEE Trans Biomed Eng – volume: 5 start-page: 110 year: 1995 end-page: 117 ident: b4 article-title: Approximate entropy (ApEn) as a complexity measure publication-title: Chaos: An Interdiscip J Nonlinear Sci – volume: 105 start-page: 4972 year: 2008 end-page: 4975 ident: b16 article-title: From time series to complex networks: The visibility graph publication-title: Proc Natl Acad Sci – volume: 72 year: 2005 ident: b21 article-title: Visualization of coupling in time series by order recurrence plots publication-title: Phys Rev E – volume: 330 start-page: 214 year: 2004 end-page: 223 ident: b22 article-title: Multivariate recurrence plots publication-title: Phys Lett A – volume: 213 year: 2023 ident: b31 article-title: Complexity measures and features for times series classification publication-title: Expert Syst Appl – year: 2024 ident: b24 article-title: Investigating optical transport network performance: A recurrence plot approach publication-title: China Commun – volume: 71 year: 2005 ident: b9 article-title: Multiscale entropy analysis of biological signals publication-title: Phys Rev E – volume: 149 year: 2021 ident: b12 article-title: Hierarchical multiscale permutation entropy-based feature extraction and fuzzy support tensor machine with pinball loss for bearing fault identification publication-title: Mech Syst Signal Process – volume: 6 start-page: 35622 year: 2016 ident: b19 article-title: Multiscale limited penetrable horizontal visibility graph for analyzing nonlinear time series publication-title: Sci Rep – volume: 111 start-page: 17299 year: 2023 end-page: 17316 ident: b32 article-title: Three-dimensional causal complementary complexity: a new measure for time series complexity analysis publication-title: Nonlinear Dynam – volume: 60 year: 2020 ident: b36 article-title: Database for an emotion recognition system based on EEG signals and various computer games–GAMEEMO publication-title: Biomed Signal Process Control – volume: 7 start-page: 203 year: 2017 end-page: 210 ident: b2 article-title: Novel algorithm for measuring the complexity of electroencephalographic signals in emotion recognition publication-title: J Med Imaging Heal Inform – volume: 147 year: 2024 ident: b3 article-title: Time pattern reconstruction for classification of irregularly sampled time series publication-title: Pattern Recognit – volume: 22 start-page: 2364 year: 2015 end-page: 2367 ident: b13 article-title: Refined composite multiscale permutation entropy to overcome multiscale permutation entropy length dependence publication-title: IEEE Signal Process Lett – volume: 29 year: 2019 ident: b26 article-title: Ordinal partition transition network based complexity measures for inferring coupling direction and delay from time series publication-title: Chaos: An Interdiscip J Nonlinear Sci – volume: 61 start-page: 725 year: 2021 end-page: 735 ident: b1 article-title: Intelligent fault identification of rotary machinery using refined composite multi-scale Lempel–Ziv complexity publication-title: J Manuf Syst – volume: 179 year: 2024 ident: b11 article-title: Optimized multivariate multiscale slope entropy for nonlinear dynamic analysis of mechanical signals publication-title: Chaos Solitons Fractals – volume: 438 start-page: 237 year: 2007 end-page: 329 ident: b17 article-title: Recurrence plots for the analysis of complex systems publication-title: Phys Rep – volume: 330 start-page: 214 issue: 3–4 year: 2004 ident: 10.1016/j.chaos.2025.117300_b22 article-title: Multivariate recurrence plots publication-title: Phys Lett A doi: 10.1016/j.physleta.2004.07.066 – year: 2024 ident: 10.1016/j.chaos.2025.117300_b24 article-title: Investigating optical transport network performance: A recurrence plot approach publication-title: China Commun doi: 10.23919/JCC.ea.2022-0180.202401 – volume: 213 year: 2023 ident: 10.1016/j.chaos.2025.117300_b31 article-title: Complexity measures and features for times series classification publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2022.119227 – volume: 80 issue: 4 year: 2009 ident: 10.1016/j.chaos.2025.117300_b18 article-title: Horizontal visibility graphs: Exact results for random time series publication-title: Phys Rev E doi: 10.1103/PhysRevE.80.046103 – volume: 72 issue: 4 year: 2005 ident: 10.1016/j.chaos.2025.117300_b21 article-title: Visualization of coupling in time series by order recurrence plots publication-title: Phys Rev E doi: 10.1103/PhysRevE.72.046220 – volume: 107 start-page: 889 issue: 1 year: 2022 ident: 10.1016/j.chaos.2025.117300_b29 article-title: Leveraging the self-transition probability of ordinal patterns transition network for transportation mode identification based on GPS data publication-title: Nonlinear Dynam doi: 10.1007/s11071-021-07059-x – volume: Vol. 384 start-page: 172 year: 2004 ident: 10.1016/j.chaos.2025.117300_b5 article-title: Sample entropy doi: 10.1016/S0076-6879(04)84011-4 – volume: 438 start-page: 237 issue: 5–6 year: 2007 ident: 10.1016/j.chaos.2025.117300_b17 article-title: Recurrence plots for the analysis of complex systems publication-title: Phys Rep doi: 10.1016/j.physrep.2006.11.001 – volume: 5 start-page: 110 issue: 1 year: 1995 ident: 10.1016/j.chaos.2025.117300_b4 article-title: Approximate entropy (ApEn) as a complexity measure publication-title: Chaos: An Interdiscip J Nonlinear Sci doi: 10.1063/1.166092 – volume: 179 year: 2024 ident: 10.1016/j.chaos.2025.117300_b11 article-title: Optimized multivariate multiscale slope entropy for nonlinear dynamic analysis of mechanical signals publication-title: Chaos Solitons Fractals doi: 10.1016/j.chaos.2023.114436 – volume: 147 year: 2024 ident: 10.1016/j.chaos.2025.117300_b3 article-title: Time pattern reconstruction for classification of irregularly sampled time series publication-title: Pattern Recognit doi: 10.1016/j.patcog.2023.110075 – volume: 33 issue: 5 year: 2023 ident: 10.1016/j.chaos.2025.117300_b28 article-title: Ordinal pattern transition networks in eye tracking reading signals publication-title: Chaos: An Interdiscip J Nonlinear Sci doi: 10.1063/5.0142230 – volume: 7 start-page: 203 issue: 1 year: 2017 ident: 10.1016/j.chaos.2025.117300_b2 article-title: Novel algorithm for measuring the complexity of electroencephalographic signals in emotion recognition publication-title: J Med Imaging Heal Inform – volume: 32 issue: 1 year: 2022 ident: 10.1016/j.chaos.2025.117300_b20 article-title: CLPVG: Circular limited penetrable visibility graph as a new network model for time series publication-title: Chaos: An Interdiscip J Nonlinear Sci doi: 10.1063/5.0048243 – year: 2020 ident: 10.1016/j.chaos.2025.117300_b35 – volume: 147 year: 2024 ident: 10.1016/j.chaos.2025.117300_b33 article-title: Scalable and accurate subsequence transform for time series classification publication-title: Pattern Recognit doi: 10.1016/j.patcog.2023.110121 – volume: 375 issue: 2096 year: 2017 ident: 10.1016/j.chaos.2025.117300_b25 article-title: Multiscale ordinal network analysis of human cardiac dynamics publication-title: Philos Trans R Soc A: Math, Phys Eng Sci doi: 10.1098/rsta.2016.0292 – volume: 111 start-page: 17299 issue: 18 year: 2023 ident: 10.1016/j.chaos.2025.117300_b32 article-title: Three-dimensional causal complementary complexity: a new measure for time series complexity analysis publication-title: Nonlinear Dynam doi: 10.1007/s11071-023-08776-1 – volume: 61 start-page: 725 year: 2021 ident: 10.1016/j.chaos.2025.117300_b1 article-title: Intelligent fault identification of rotary machinery using refined composite multi-scale Lempel–Ziv complexity publication-title: J Manuf Syst doi: 10.1016/j.jmsy.2020.05.004 – volume: 152 year: 2024 ident: 10.1016/j.chaos.2025.117300_b34 article-title: MTS2graph: Interpretable multivariate time series classification with temporal evolving graphs publication-title: Pattern Recognit doi: 10.1016/j.patcog.2024.110486 – volume: 60 year: 2020 ident: 10.1016/j.chaos.2025.117300_b36 article-title: Database for an emotion recognition system based on EEG signals and various computer games–GAMEEMO publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2020.101951 – volume: 43 start-page: 887 issue: 3 year: 2010 ident: 10.1016/j.chaos.2025.117300_b30 article-title: GeoEntropy: A measure of complexity and similarity publication-title: Pattern Recognit doi: 10.1016/j.patcog.2009.08.015 – volume: 88 issue: 17 year: 2002 ident: 10.1016/j.chaos.2025.117300_b7 article-title: Permutation entropy: a natural complexity measure for time series publication-title: Phys Rev Lett doi: 10.1103/PhysRevLett.88.174102 – volume: 71 issue: 2 year: 2005 ident: 10.1016/j.chaos.2025.117300_b9 article-title: Multiscale entropy analysis of biological signals publication-title: Phys Rev E doi: 10.1103/PhysRevE.71.021906 – volume: 22 start-page: 2364 issue: 12 year: 2015 ident: 10.1016/j.chaos.2025.117300_b13 article-title: Refined composite multiscale permutation entropy to overcome multiscale permutation entropy length dependence publication-title: IEEE Signal Process Lett doi: 10.1109/LSP.2015.2482603 – volume: 29 issue: 4 year: 2019 ident: 10.1016/j.chaos.2025.117300_b26 article-title: Ordinal partition transition network based complexity measures for inferring coupling direction and delay from time series publication-title: Chaos: An Interdiscip J Nonlinear Sci doi: 10.1063/1.5086527 – volume: 64 start-page: 2711 issue: 11 year: 2017 ident: 10.1016/j.chaos.2025.117300_b8 article-title: Bubble entropy: An entropy almost free of parameters publication-title: IEEE Trans Biomed Eng doi: 10.1109/TBME.2017.2664105 – volume: 149 year: 2021 ident: 10.1016/j.chaos.2025.117300_b12 article-title: Hierarchical multiscale permutation entropy-based feature extraction and fuzzy support tensor machine with pinball loss for bearing fault identification publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2020.107182 – volume: 68 start-page: 2530 issue: 4 year: 2022 ident: 10.1016/j.chaos.2025.117300_b14 article-title: Generalized maximum entropy for supervised classification publication-title: IEEE Trans Inform Theory doi: 10.1109/TIT.2022.3143764 – volume: 140 year: 2022 ident: 10.1016/j.chaos.2025.117300_b15 article-title: Automated classification of attention deficit hyperactivity disorder and conduct disorder using entropy features with ECG signals publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2021.105120 – volume: 21 start-page: 541 issue: 6 year: 2019 ident: 10.1016/j.chaos.2025.117300_b6 article-title: Approximate entropy and sample entropy: A comprehensive tutorial publication-title: Entropy doi: 10.3390/e21060541 – volume: 105 start-page: 4972 issue: 13 year: 2008 ident: 10.1016/j.chaos.2025.117300_b16 article-title: From time series to complex networks: The visibility graph publication-title: Proc Natl Acad Sci doi: 10.1073/pnas.0709247105 – volume: 392 start-page: 5865 issue: 23 year: 2013 ident: 10.1016/j.chaos.2025.117300_b10 article-title: Modified multiscale entropy for short-term time series analysis publication-title: Phys A doi: 10.1016/j.physa.2013.07.075 – volume: 6 start-page: 35622 issue: 1 year: 2016 ident: 10.1016/j.chaos.2025.117300_b19 article-title: Multiscale limited penetrable horizontal visibility graph for analyzing nonlinear time series publication-title: Sci Rep doi: 10.1038/srep35622 – start-page: 19 year: 2016 ident: 10.1016/j.chaos.2025.117300_b23 article-title: Applications of transient signal analysis using the concept of recurrence plot analysis – year: 2024 ident: 10.1016/j.chaos.2025.117300_b27 article-title: Detecting tendency behavior of cellular network traffic via ordinal pattern transition networks publication-title: IEEE Internet Things J doi: 10.1109/JIOT.2024.3440332 |
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