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Time Series Classification Based on Complex Network

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Graph Data Mining

Part of the book series: Big Data Management ((BIGDM))

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Abstract

Time series classification plays an important role in various tasks such as electroencephalogram (EEG) classification, electrocardiogram (ECG) classification, human activity recognition and radio signal modulation identification. At present, some promising methods have been proposed to transform time series classification into graph classification by mapping time series to graphs. However, these transformation methods with fixed mapping rules may lack of flexibility, leading to the loss of information, so as to decrease the classification accuracy. In this chapter, we introduce circular limited penetrable visibility graph (CLPVG), a new method of mapping time series to graphs. Furthermore, in order to map time series to graphs more flexibly through deep learning, we also introduce an automatic visibility graph (AVG) based on graph neural network (GNN), a framework which can transform time series into graphs and realize classification end-to-end. Finally, we carry out experiments on some common datasets to prove the effectiveness of our methods.

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References

  1. Wong, M.D., Nandi, A.K.: Automatic digital modulation recognition using artificial neural network and genetic algorithm. Signal Process. 84(2), 351–365 (2004)

    Article  MATH  Google Scholar 

  2. Dobre, O.A., Abdi, A., Bar-Ness, Y., Su, W.: Survey of automatic modulation classification techniques: classical approaches and new trends. IET Commun. 1(2), 137–156 (2007)

    Article  Google Scholar 

  3. Ahmadlou, M., Adeli, H., Adeli, A.: Improved visibility graph fractality with application for the diagnosis of autism spectrum disorder. Physica A: Stat. Mech. Appl. 391(20), 4720–4726 (2012)

    Article  Google Scholar 

  4. Wang, J., Yang, C., Wang, R., Yu, H., Cao, Y., Liu, J.: Functional brain networks in alzheimer’s disease: Eeg analysis based on limited penetrable visibility graph and phase space method. Physica A: Stat. Mech. Appl. 460, 174–187 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  5. Gao, Z.-K., Cai, Q., Yang, Y.-X., Dang, W.-D., Zhang, S.-S.: Multiscale limited penetrable horizontal visibility graph for analyzing nonlinear time series. Sci. Rep. 6, 35622 (2016)

    Article  Google Scholar 

  6. Pei, X., Wang, J., Deng, B., Wei, X., Yu, H.: WLPVG approach to the analysis of EEG-based functional brain network under manual acupuncture. Cogn. Neurodyn. 8(5), 417–428 (2014)

    Article  Google Scholar 

  7. Supriya, S., Siuly, S., Wang, H., Cao, J., Zhang, Y.: Weighted visibility graph with complex network features in the detection of epilepsy. IEEE Access 4, 6554–6566 (2016)

    Article  Google Scholar 

  8. Soliman, S.S., Hsue, S.-Z.: Signal classification using statistical moments. IEEE Trans. Commun. 40(5), 908–916 (1992)

    Article  MATH  Google Scholar 

  9. Subasi, A.: Eeg signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst. Appl. 32(4), 1084–1093 (2007)

    Article  Google Scholar 

  10. Bracewell, R.N., Bracewell, R.N.: The Fourier Transform and its Applications, vol. 31999 (McGraw-Hill, New York, 1986)

    MATH  Google Scholar 

  11. Antonini, M., Barlaud, M., Mathieu, P., Daubechies, I.: Image coding using wavelet transform. IEEE Trans. Image Process. 1(2), 205–220 (1992)

    Article  Google Scholar 

  12. B. Mollow, Power spectrum of light scattered by two-level systems. Phys. Rev. 188(5), 1969–1975 (1969)

    Article  Google Scholar 

  13. Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)

    Google Scholar 

  14. Liaw, A., Wiener, M. et al.: Classification and regression by randomforest. R news 2(3), 18–22 (2002)

    Google Scholar 

  15. Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural. Process. Lett. 9(3), 293–300 (1999)

    Article  Google Scholar 

  16. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  17. Wang, Z., Yan, W., Oates, T.: Time series classification from scratch with deep neural networks: A strong baseline. In: Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), pp. 1578–1585 (IEEE, New York, 2017)

    Google Scholar 

  18. O’Shea, T.J., Corgan, J., Clancy, T.C.: Convolutional radio modulation recognition networks. In: International Conference on Engineering Applications of Neural Networks, pp. 213–226 (Springer, Berlin, 2016)

    Google Scholar 

  19. Zaremba, W., Sutskever, I., Vinyals, O.: Recurrent neural network regularization. arXiv preprint arXiv:1409.2329 (2014)

    Google Scholar 

  20. Hüsken, M., Stagge, P.: Recurrent neural networks for time series classification. Neurocomputing 50, 223–235 (2003)

    Article  MATH  Google Scholar 

  21. Malhotra, P., TV, V., Vig, L., Agarwal, P., Shroff, G.: Timenet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838 (2017)

    Google Scholar 

  22. Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: the visibility graph. Proc. Natl. Acad. Sci. 105(13), 4972–4975 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  23. Xie, W.-J., Han, R.-Q., Zhou, W.-X.: Tetradic motif profiles of horizontal visibility graphs. Commun. Nonlinear Sci. Numer. Simul. 72, 544–551 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  24. Cai, L., Wang, J., Cao, Y., Deng, B., Yang, C.: LPVG analysis of the EEG activity in alzheimer’s disease patients, in Proceedings of the 2016 12th World Congress on Intelligent Control and Automation (WCICA) (2016)

    Google Scholar 

  25. Lee, K.-F.: Automatic Speech Recognition: The Development of the SPHINX System, vol. 62 (Springer, Berlin, 1988)

    Google Scholar 

  26. Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., Muller, P.-A.: Deep learning for time series classification: a review. Data Min. Knowl. Disc. 33(4), 917–963 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  27. Nanopoulos, A., Alcock, R., Manolopoulos, Y.: Feature-based classification of time-series data. Int. J. Comput. Res. 10(3), 49–61 (2001)

    Google Scholar 

  28. Deng, H., Runger, G., Tuv, E., Vladimir, M.: A time series forest for classification and feature extraction. Inf. Sci. 239, 142–153 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  29. Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: KDD Workshop, vol. 10, pp. 359–370, Seattle, WA, USA (1994)

    Google Scholar 

  30. Ristad, E.S., Yianilos, P.N.: Learning string-edit distance. IEEE Trans. Pattern Anal. Mach. Intell. 20(5), 522–532 (1998)

    Article  Google Scholar 

  31. Hunt, J.W., Szymanski, T.G.: A fast algorithm for computing longest common subsequences. Commun. ACM 20(5), 350–353 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  32. Wang, Z., Yan, W., Oates, T.: Time series classification from scratch with deep neural networks: A strong baseline. In: Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), pp. 1578–1585 (2017)

    Google Scholar 

  33. Karim, F., Majumdar, S., Darabi, H., Chen, S.: LSTM fully convolutional networks for time series classification. IEEE Access 6, 1662–1669 (2017)

    Article  Google Scholar 

  34. Zhang, M., Cui, Z., Neumann, M., Chen, Y.: An end-to-end deep learning architecture for graph classification. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

  35. Lee, J.B., Rossi, R., Kong, X.: Graph classification using structural attention. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1666–1674 (2018)

    Google Scholar 

  36. Riesen, K., Bunke, H.: Graph classification based on vector space embedding. Int. J. Pattern Recognit. Artif. Intell. 23(06), 1053–1081 (2009)

    Article  MATH  Google Scholar 

  37. Kudo, T., Maeda, E., Matsumoto, Y.: An application of boosting to graph classification. In: Advances in Neural Information Processing Systems, pp. 729–736 (2005)

    Google Scholar 

  38. Costa, L.d.F., Rodrigues, F.A., Travieso, G., Boas, P.R.V.: Characterization of Complex Networks: A Survey of Measurements (2005)

    Google Scholar 

  39. Narayanan, A., Chandramohan, M., Venkatesan, R., Chen, L., Liu, Y., Jaiswal, S.: graph2vec: learning distributed representations of graphs. arXiv preprint arXiv:1707.05005 (2017)

    Google Scholar 

  40. Lau, J.H., Baldwin, T.: An empirical evaluation of doc2vec with practical insights into document embedding generation. arXiv preprint arXiv:1607.05368 (2016)

    Google Scholar 

  41. Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, pp. 1024–1034 (2017)

    Google Scholar 

  42. Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W., Leskovec, J.: Hierarchical graph representation learning with differentiable pooling. In: Advances in Neural Information Processing Systems, pp. 4800–4810 (2018)

    Google Scholar 

  43. Xuan, Q., Wang, J., Zhao, M., Yuan, J., Fu, C., Ruan, Z., Chen, G.: Subgraph networks with application to structural feature space expansion. IEEE Trans. Knowl. Data Eng. 33(6), 2776–2789 (2021)

    Article  Google Scholar 

  44. O’Shea, T.J., Corgan, J., Clancy, T.C.: Convolutional radio modulation recognition networks. In: International Conference on Engineering Applications of Neural Networks, pp. 213–226 (Springer, Berlin, 2016)

    Google Scholar 

  45. Andrzejak, R.G., Lehnertz, K., Mormann, F., Rieke, C., David, P., Elger, C.E.: Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Phys. Rev. E 64(6), 061907 (2001)

    Article  Google Scholar 

  46. Dau, H.A., Bagnall, A., Kamgar, K., Yeh, C.-C.M., Zhu, Y., Gharghabi, S., Ratanamahatana, C.A., Keogh, E.: The UCR time series archive. IEEE/CAA J. Automat. Sin. 6(6), 1293–1305 (2019)

    Article  Google Scholar 

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Correspondence to Qi Xuan .

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Qiu, K., Zhou, J., Cui, H., Chen, Z., Zheng, S., Xuan, Q. (2021). Time Series Classification Based on Complex Network. In: Xuan, Q., Ruan, Z., Min, Y. (eds) Graph Data Mining. Big Data Management. Springer, Singapore. https://doi.org/10.1007/978-981-16-2609-8_10

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  • DOI: https://doi.org/10.1007/978-981-16-2609-8_10

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