Applying Recurrence Plots to Classify Time Series

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dc.citation.epage26
dc.citation.spage16
dc.contributor.affiliationKharkiv National University of Radio Electronics
dc.contributor.authorKirichenko, Lyudmyla
dc.contributor.authorRadivilova, Tamara
dc.contributor.authorStepanenko, Juliia
dc.coverage.placenameЛьвів ; Харків
dc.coverage.placenameLviv ; Kharkiv
dc.coverage.temporal22-23 April 2021, Kharkiv
dc.date.accessioned2022-05-23T10:50:16Z
dc.date.available2022-05-23T10:50:16Z
dc.date.created2021-05-04
dc.date.issued2021-05-04
dc.description.abstractThe article describes a new approach to the classification of time series based on the construction of their recurrence plots. After transforming the time series into recurrence plots, two approaches are applied for classification. In the first case, quantitative recurrence characteristics are used for classification as features. In the second case, the time series is presented in the form of a black and white image of its recurrence plot. A convolutional neural network is used as an image classifier. The data for the classification are the electrocardiograms realizations of 100 values, which contained records of healthy people and patients with a diagnosis of ischemia. Research results showed the advantages of classifying images of recurrence plots, indicate a good classification accuracy in comparison with other methods and the potential capabilities of this approach.
dc.format.extent16-26
dc.format.pages11
dc.identifier.citationKirichenko L. Applying Recurrence Plots to Classify Time Series / Lyudmyla Kirichenko, Tamara Radivilova, Juliia Stepanenko // Computational linguistics and intelligent systems, 22-23 April 2021, Kharkiv. — Lviv ; Kharkiv, 2021. — Vol Vol. II : Proceedings of the 5th International conference, COLINS 2021, Workshop, Kharkiv, Ukraine, April 22-23. — P. 16–26.
dc.identifier.citationenKirichenko L. Applying Recurrence Plots to Classify Time Series / Lyudmyla Kirichenko, Tamara Radivilova, Juliia Stepanenko // Computational linguistics and intelligent systems, 22-23 April 2021, Kharkiv. — Lviv ; Kharkiv, 2021. — Vol Vol. II : Proceedings of the 5th International conference, COLINS 2021, Workshop, Kharkiv, Ukraine, April 22-23. — P. 16–26.
dc.identifier.issn2523-4013
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/56817
dc.language.isoen
dc.relation.ispartofComputational linguistics and intelligent systems, 2021
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dc.relation.references[4] L. Kirichenko, T. Radivilova, V. Bulakh. Binary Classification of Fractal Time Series by Machine Learning Methods, in: V. Lytvynenko, S. Babichev, W. Wójcik, O. Vynokurova, S. Vyshemyrskaya, S. Radetskaya (Eds.), Lecture Notes in Computational Intelligence and Decision Making, volume 1020 of Advances in Intelligent Systems and Computing, Springer, Cham, 2020, pp. 701-711. doi: 10.1007/978-3-030-26474-1_49
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dc.relation.references[6] L. Kirichenko, P. Zinchenko, T. Radivilova, M. Tavalbeh, Machine Learning Detection of DDoS Attacks Based on Visualization of Recurrence Plots, in: Proceedings of the International Workshop of Conflict Management in Global Information Networks, CMiGIN 2019, Ceur, Kyiv, Ukraine, 2019, pp. 23–34.
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dc.relation.references[16] Time series classification. URL: http://www.timeseriesclassification.com
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dc.relation.references[19] S. Ioffe and C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, in: Proceedings of the 32nd International Conference on Machine Learning, volume 37, 2015, pp. 448-456.
dc.relation.references[20] D. P. Kingma and J. Ba Adam, A Method for Stochastic Optimization, in: Proceedings of the 3rd International Conference on Learning Representations (ICLR), 2015.
dc.relation.referencesen[1] J. C. B. Gamboa, Deep learning for time-series analysis, 2017. URL: https://arxiv.org/pdf/1701.01887.pdf.
dc.relation.referencesen[2] H. Ismail Fawaz, G. Forestier, J. Weber, L. Idoumghar, and P. A. Muller, Deep learning for time series classification: a review. Data Mining and Knowledge Discovery 33.4 (2019): 917-963. doi: 10.1007/s10618-019-00619-1.
dc.relation.referencesen[3] Marisa Faraggi, Time series features extraction using Fourier and Wavelet transforms on ECG data. URL: https://slacker.ro/2019/11/23/time-series-features-extraction-using-fourier-andwavelet-transforms-on-ecg-dat.
dc.relation.referencesen[4] L. Kirichenko, T. Radivilova, V. Bulakh. Binary Classification of Fractal Time Series by Machine Learning Methods, in: V. Lytvynenko, S. Babichev, W. Wójcik, O. Vynokurova, S. Vyshemyrskaya, S. Radetskaya (Eds.), Lecture Notes in Computational Intelligence and Decision Making, volume 1020 of Advances in Intelligent Systems and Computing, Springer, Cham, 2020, pp. 701-711. doi: 10.1007/978-3-030-26474-1_49
dc.relation.referencesen[5] T. Radivilova, L. Kirichenko, V. Bulakh, Comparative analysis of machine learning classification of time series with fractal properties, in: Proceedings of 8th International Conference on Advanced Optoelectronics and Lasers, CAOL 2019, IEEE, Sozopol, Bulgaria, 2019, pp. 557-560. doi: 10.1109/CAOL46282.2019.9019416
dc.relation.referencesen[6] L. Kirichenko, P. Zinchenko, T. Radivilova, M. Tavalbeh, Machine Learning Detection of DDoS Attacks Based on Visualization of Recurrence Plots, in: Proceedings of the International Workshop of Conflict Management in Global Information Networks, CMiGIN 2019, Ceur, Kyiv, Ukraine, 2019, pp. 23–34.
dc.relation.referencesen[7] L. Kirichenko, P. Zinchenko, T. Radivilova, Classification of Time Realizations Using Machine Learning Recognition of Recurrence Plots, in: S. Babichev, V. Lytvynenko, W. Wójcik, S. Vyshemyrskaya (Eds.), Lecture Notes in Computational Intelligence and Decision Making, volume 1246, of Advances in Intelligent Systems and Computing, Springer, Cham, 2021, pp. 687-696. doi: 10.1007/978-3-030-54215-3_44.
dc.relation.referencesen[8] N. Hatami, Y Gavet, J. Debayle, Classification of time-series images using deep convolutional neural networks, in: Proceedings of Tenth International Conference on Machine Vision, ICMV 2017, 10696, 106960Y, 2018.
dc.relation.referencesen[9] J. P. Eckmann, S. O. Kamphorst, D. Ruelle, Recurrence plots of dynamical systems. Europhysics Letters 4.9, (1987): 973-977.
dc.relation.referencesen[10] N. Marwan, N. Wessel, U. Meyerfeldt, A. Schirdewan, J. Kurths, Recurrence-plots-based measures of complexity and application to heart-rate-variability data. Physical Review E 66.2 (2002): 026702-1-026702-6. doi: 10.1103/PhysRevE.66.026702
dc.relation.referencesen[11] N. Marwan, M. C. Romano, M. Thiel, J. Kurths, Recurrence plots for the analysis of complex systems. Physics reports 438.5-6 (2007): 237-329.
dc.relation.referencesen[12] L. Kirichenko, T. Radivilova, V. Bulakh, Classification of fractal time series using recurrence plots, in: Proceedings of 2018 International Scientific-Practical Conference Problems of Infocommunications. Science and Technology, PIC S&T 2018, IEEE, Kharkiv, Ukraine, 2018, pp.719-724. doi: 10.1109/INFOCOMMST.2018.8632010.
dc.relation.referencesen[13] L. Kirichenko, T. Radivilova, V. Bulakh, P. Zinchenko and A. Saif Alghawli, Two Approaches to Machine Learning Classification of Time Series Based on Recurrence Plots, 2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine, 2020, pp. 84-89, doi: 10.1109/DSMP47368.2020.9204021.
dc.relation.referencesen[14] Y. LeCun and Y. Bengio, Convolutional Networks for Images, Speech, and Time-Series, in M. A. Arbib (Eds.), The Handbook of Brain Theory and Neural Networks, MIT Press, 1995.
dc.relation.referencesen[15] C. Dan, U. Meier, J. Masci, L. M. Gambardella, J. Schmidhuber, Flexible, High Performance Convolutional Neural Networks for Image Classification, in: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, volume 2, 2013, pp.1237–1242. URL: http://people.idsia.ch/~juergen/ijcai2011.pdf.
dc.relation.referencesen[16] Time series classification. URL: http://www.timeseriesclassification.com
dc.relation.referencesen[17] D. Cielen, A. Meysman, M. Ali, Introducing Data Science: Big Data, Machine Learning, and more, using Python tools, Manning Publications, 2016.
dc.relation.referencesen[18] J. Brownlee, A Gentle Introduction to the Rectified Linear Unit (ReLU), Machine learning mastery, January 2019. URL: https://machinelearningmastery.com/rectified-linear-activationfunction-for-deep-learning-neural-networks
dc.relation.referencesen[19] S. Ioffe and C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, in: Proceedings of the 32nd International Conference on Machine Learning, volume 37, 2015, pp. 448-456.
dc.relation.referencesen[20] D. P. Kingma and J. Ba Adam, A Method for Stochastic Optimization, in: Proceedings of the 3rd International Conference on Learning Representations (ICLR), 2015.
dc.relation.urihttps://arxiv.org/pdf/1701.01887.pdf
dc.relation.urihttps://slacker.ro/2019/11/23/time-series-features-extraction-using-fourier-andwavelet-transforms-on-ecg-dat
dc.relation.urihttp://people.idsia.ch/~juergen/ijcai2011.pdf
dc.relation.urihttp://www.timeseriesclassification.com
dc.relation.urihttps://machinelearningmastery.com/rectified-linear-activationfunction-for-deep-learning-neural-networks
dc.rights.holdercopyrighted by its editors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
dc.rights.holder© 2021 Copyright for the individual papers by the papers’ authors. Copying permitted only for private and academic purposes. This volume is published and
dc.subjectTime series classification
dc.subjectmachine learning classification
dc.subjectrecurrence plot
dc.subjectECG time series
dc.subjectquantitative recurrence characteristic
dc.titleApplying Recurrence Plots to Classify Time Series
dc.typeArticle

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