Special issue: The 10th International Conference on Awareness Science and Technology (iCAST 2019)

Hidetoshi Ito1, Basabi Chakraborty2*

1 Graduate School of Software and Information Science, Iwate Prefectural University, Iwate, Japan
2 Faculty of Software and Information Science, Iwate Prefectural University, Takizawa, Iwate, Japan


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This work is an extended version of the paper published by Ito and Chakraborty (2019). Time Series Classification (TSC) is gaining importance in the area of pattern recognition, as the availability of time series data has been increased recently. TSC is a complicated problem because of needs to consider the characteristics of temporal data; periodicity, time correlation, elasticity and unequal lengths of the time series. As all of those characteristics are usually not expressed simultaneously in raw data, design of a unified similarity metric for time series classification or clustering is difficult to achieve. In addition to traditional feature-based, model-based or distance-based algorithms for TSC, ensemble and deep neural network have been proposed recently, and deep neural network model like ResNet is known to be quite effective. However, deep neural network model requires enormous computing resources and computing time as well as large number of training samples. Feature based and distance based approaches till have potential to outperform them in computational time with reasonable classification accuracy. In this work, new temporal data transformation algorithms have been proposed and their combination with nearest neighbor classifier have been compared to existing time series classification methods. From the experimental results, the proposed algorithms with nearest neighbor classifier are found to be inferior to ResNet regarding classification accuracy though comparable to Dynamic Time Warping (DTW) but the computation is much faster than ResNet and DTW, and also the classification accuracy is better in case of small datasets which seems to be important for many real life applications with limited resources.

Keywords: Time series classification; feature extraction; deep neural network.

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  1. Al-Naymat, G., Chawla, S. Taheri, J. 12/2009. Sparse DTW: A novel approach to speed up dynamic time warping, The 2009 Australasian Data Mining, vol. 101, Melbourne, Australia, ACM Digital Library, 117–127.

  2. Antonucci, A., DeRosa, R., Giusti, A. et al., 2015. Robust classification of multivariate time series by imprecise hidden Markov models, International Journal of Approximate Reasoning, 56(B), 249–263.

  3. Bagnall, A., Bostrom, A., Large, J, Lines, J. 2017. The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances, Data Mining and Knowledge Discovery, 31, 606–660.

  4. Bagnall, A., Lines, J., Hills, J., Bostrom, A. 2015. Time series classification with COTE: The collective of transform-based ensembles, IEEE Trans. on Knowledge and Data Engineering, 27, 2522–2535.

  5. Bagnall, A.J., Bostrom, A., Cawley, G.C., Flynn, M., Large, J., Lines, J. 2018. Is rotation forest the best classifier for problems with continuous features?, ArXiv, vol. abs/1809.06705.

  6. Baydogan, M.G., Runger, G., Tuv, E. 2013. A bag-of-features framework to classify time series, IEEE Trans. on PAMI, 35, 2796–2802.

  7. Bishop, Christopher, M. 1995. Neural networks for pattern recognition. Oxford university press.

  8. Chakraborty B., Yoshida, S. 2016. Proposal of a new similarity measure for time series classification, Proc. ITISE 2016, Spain.

  9. Chakraborty, B., Yoshida, S. 2017. A novel genetic algorithm based similarity measure for time series classification, Proceedings of ITISE 2017, 536–547.

  10. Dau, H.A., Keogh, E., Kamgar, K., Yeh, C.-C.M., Zhu, Y., Gharghabi, S., Ratanamahatana, C.A., Chen, Y., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G., Hexagon-ML, 2019. The UCR time series classification archive. URL https://www.cs.ucr.edu/~eamonn/time_

  11. Deng, H., Runger, G.C., Tuv, E. Martyanov, V. 2013. A time series forest for classification and feature extraction, Inf. Sci., 239, 142–153.

  12. Ebenezer, R.H.P., Isaaca, Susan Elias, Srinivasan Rajagopalan, Easwarakumar, K.S. 2019. Trait of Gait: A Survey on Gait Biometrics’, https://arxiv.org/pdf/1903.10744.pdf

  13. Fawaz, H.I., Forestier, G., Weber J., Idoumghar, L., Muller, P.A. 2019. Deep learning for time series classification :a review, Data Mining and Knowledge Discovery, 33, 917–963.

  14. Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A. 2019. Deep learning for time series classification :a review, Data Mining and Knowledge Discovery, 1–47.

  15. Fisher, T., Krauss, C. 2018. Deep learning with long short-term memory networks for financial market predictions. Eur. J. Oper. Res., 270, 654–669.

  16. He, K., Zhang, X., Ren, S., Sun, J., NV, 2016. Deep residual learning for image recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 770–778.

  17. Ito, H., Chakraborty, B. 2018. A proposal for cost aware edge-detectional dynamic time warping for time series classification, in Proceedings of iCAST 2018, 150–154.

  18. Ito, H., Chakraborty, B. 2019. A proposal for shape aware feature extraction for time series classification, 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST), Morioka, Japan, 1–6.

  19. Karim, F., Majumdar, S., Darabi, H., Chen, S. 2018. LSTM fully convolutional networks for time series classification. In: IEEE Access 6, 1662–1669.

  20. Keogh, E.J., Pazzani, M.J. 2000. Scaling up dynamic time warping for datamining applications, Proceedings of KDD, 285–289.

  21. Kini, B.V., Sekhar, C.C. 2013. Large margin mixture of AR models for time series classification, Applied Soft Computing, 13, 361–371.

  22. Lara, O.D., Labrador, M. 2013. A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutor. 15, 1192–1209.

  23. Lines, J., Bagnall, A. 2015. Time series classification with ensembles of elastic distance measures, Data Mining and Knowledge Discovery, 29, 565–592.

  24. Lines, J., Taylor, S., Bagnall, A. 2018. Time series classification with HIVE-COTE: The hierarchical vote collective of transformation based ensembles, ACM Transactions on Knowledge Discovery from Data, 12, 1–52.35.

  25. Sakoe, H., Chiba, S., Feb 1978. Dynamic programming algorithm optimization for spoken word recognition, in IEEE Transactions on Acoustics, Speech, and Signal Processing, 26, 43–49.

  26. Salvador, S., Chan, P. 2007. FastDTW : Toward accurate dynamic time warping in linear time and space, Intell. Data Anal., 11, 561–580. series_data_2018/

  27. Smirnov, D., Nguifo, E.M. 2018. Time series classification with recurrent neural networks’, https://project.inria.fr/aaldt18/files/2018/08/aaltd18rnn.pdf

  28. Tamilarasi, K., Nithya Kalyani, S., April 2017. A survey on signature verification based algorithms. In Proceedings of the IEEE International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE), Karur, India, 27–28, 1–3.

  29. Wang J., Liu P., She, M., Nahavandi S., Kouzani A. 2012. Bag-of-Words representation for biomedical time series classification. Biomedical Signal Processing and Control. 8. 10.1016/j.bspc.2013.06.004.

  30. Wang, Z., Yan, W., Oates, T., May, 2017. Time series classification from scratch with deep neural networks: A strong base line. In Proceedings of IEEE IJCNN, Alaska, USA, 14–19, 1578–1585.

  31. Wilcoxon, F. 1945. Individual comparisons by ranking methods. Biometrics Bulletin, 1, 80–83.

  32. Ye, L., Keogh, E.L., Ye, Keogh, E.J. 2010. Time series shapelets: a novel technique that allows accurate, interpretable and fast classification, Data Mining and Knowledge Discovery, 22, 149–182.


Received: 2020-05-19

Accepted: 2020-07-28
Publication Date: 2020-09-01

Cite this article:

Ito, H., Chakraborty, B. 2020. Fast and interpretable transformation for time series classification: A comparative study. International Journal of Applied Science and Engineering, 17, 269–280. https://doi.org/10.6703/IJASE.202009_17(3).269

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