https://oldena.lpnu.ua/handle/ntb/56817| Title: | Applying Recurrence Plots to Classify Time Series |
| Authors: | Kirichenko, Lyudmyla Radivilova, Tamara Stepanenko, Juliia |
| Affiliation: | Kharkiv National University of Radio Electronics |
| Bibliographic description (Ukraine): | Kirichenko 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. |
| Bibliographic description (International): | Kirichenko 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. |
| Is part of: | Computational linguistics and intelligent systems, 2021 |
| Issue Date: | 4-May-2021 |
| Place of the edition/event: | Львів ; Харків Lviv ; Kharkiv |
| Temporal Coverage: | 22-23 April 2021, Kharkiv |
| Keywords: | Time series classification machine learning classification recurrence plot ECG time series quantitative recurrence characteristic |
| Number of pages: | 11 |
| Page range: | 16-26 |
| Start page: | 16 |
| End page: | 26 |
| Abstract: | The 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. |
| URI: | https://ena.lpnu.ua/handle/ntb/56817 |
| ISSN: | 2523-4013 |
| Copyright owner: | copyrighted by its editors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). © 2021 Copyright for the individual papers by the papers’ authors. Copying permitted only for private and academic purposes. This volume is published and |
| URL for reference material: | https://arxiv.org/pdf/1701.01887.pdf https://slacker.ro/2019/11/23/time-series-features-extraction-using-fourier-andwavelet-transforms-on-ecg-dat http://people.idsia.ch/~juergen/ijcai2011.pdf http://www.timeseriesclassification.com https://machinelearningmastery.com/rectified-linear-activationfunction-for-deep-learning-neural-networks |
| References (Ukraine): | [1] J. C. B. Gamboa, Deep learning for time-series analysis, 2017. URL: https://arxiv.org/pdf/1701.01887.pdf. [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. [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. [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 [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 [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. [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. [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. [9] J. P. Eckmann, S. O. Kamphorst, D. Ruelle, Recurrence plots of dynamical systems. Europhysics Letters 4.9, (1987): 973-977. [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 [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. [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. [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. [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. [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. [16] Time series classification. URL: http://www.timeseriesclassification.com [17] D. Cielen, A. Meysman, M. Ali, Introducing Data Science: Big Data, Machine Learning, and more, using Python tools, Manning Publications, 2016. [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 [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. [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. |
| References (International): | [1] J. C. B. Gamboa, Deep learning for time-series analysis, 2017. URL: https://arxiv.org/pdf/1701.01887.pdf. [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. [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. [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 [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 [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. [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. [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. [9] J. P. Eckmann, S. O. Kamphorst, D. Ruelle, Recurrence plots of dynamical systems. Europhysics Letters 4.9, (1987): 973-977. [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 [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. [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. [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. [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. [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. [16] Time series classification. URL: http://www.timeseriesclassification.com [17] D. Cielen, A. Meysman, M. Ali, Introducing Data Science: Big Data, Machine Learning, and more, using Python tools, Manning Publications, 2016. [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 [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. [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. |
| Content type: | Article |
| Appears in Collections: | Computational linguistics and intelligent systems. – 2021 р. |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.