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Robust Scale-Invariant Normalization and Similarity Measurement for Time Series Data

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Modern Approaches for Intelligent Information and Database Systems

Abstract

Classification is one of the most prevalent tasks in time series mining. Dynamic Time Warping and Longest Common Subsequence are well-known and widely used algorithms to measure similarity between two time series sequences using non-linear alignment. However, these algorithms work at its best when the time series pair has similar amplitude scaling, as a little adjustment of scale can actually double the error rates. Unfortunately, sensor data and most real-world time series data usually contain noise, missing values, outlier, and variability or scaling in both axes, which is not suitable for the widely used Z-normalization. We introduce the Local Feature Normalization (LFN) and its Local Scaling Feature (LSF), which can be used to robustly normalize noisy/warped/missing-valued time series. In addition, we utilize LSF to match time series containing multiple subsequences with a variety of scales; this algorithm is called Longest Common Local Scaling Feature (LCSF). Comparing to the usage of Z-normalized data, our classification results show that our proposed LFN is impressively robust, especially on high-error and noisy datasets. On both synthetic and real application data for wrist strengthening rehabilitation exercise using a mobile phone sensor, our LCSF similarity measure also significantly outperforms other existing methods by a large margin.

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References

  1. Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26, 43–49 (1978)

    Article  MATH  Google Scholar 

  2. Vlachos, M., Hadjieleftheriou, M., Gunopulos, D., Keogh, E.: Indexing multi-dimensional time-series with support for multiple distance measures. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 216–225. ACM, New York, USA (2003)

    Google Scholar 

  3. Das, G., Gunopulos, D., Mannila, H.: Finding similar time series. In: Principles of Data Mining and Knowledge Discovery, pp. 88–100. Springer, Berlin, Heidelberg (1997)

    Google Scholar 

  4. Rakthanmanon, T., Campana, B., Mueen, A., Batista, G., Westover, B., Zhu, Q., Zakaria, J., Keogh, E.: Searching and mining trillions of time series subsequences under dynamic time warping. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 262–270. ACM, New York, USA (2012)

    Google Scholar 

  5. Gavrila, D.M., Davis, L.S.: 3-D model-based tracking of human upper body movement: a multi-view approach. In: Proceedings of International Symposium on Computer Vision—ISCV, pp. 253–258 (1995)

    Google Scholar 

  6. Crouch, D., Huang, H.: Simple EMG-driven musculoskeletal model enables consistent control performance during path tracing tasks. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1–4 (2016)

    Google Scholar 

  7. Yun, Y., Dancausse, S., Esmatloo, P., Serrato, A., Merring, C.A., Agarwal, P., Deshpande, A.D.: Maestro: an EMG-driven assistive hand exoskeleton for spinal cord injury patients. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 2904–2910 (2017)

    Google Scholar 

  8. Chen, X., Zhang, X., Zhao, Z.Y., Yang, J.H., Lantz, V., Wang, K.Q.: Hand gesture recognition research based on surface EMG sensors and 2D-accelerometers. In: 2007 11th IEEE International Symposium on Wearable Computers, pp. 11–14 (2007)

    Google Scholar 

  9. Fredman, M.L.: On computing the length of longest increasing subsequences. Discret. Math. 11, 29–35 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  10. Peterson, L.E.: K-nearest neighbor. Scholarpedia 4, 1883 (2009)

    Article  Google Scholar 

  11. Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR Time Series Classification Archive. http://www.cs.ucr.edu/~eamonn/time_series_data/

  12. Keogh, E.J., Pazzani, M.J.: Derivative dynamic time warping. In: First SIAM International Conference on Data Mining (SDM’2001 (2001)

    Google Scholar 

  13. Lucey, S.: Patient education flyers and videos about orthopedic conditions and treatments. http://www.smjrortho.com/education.php

  14. Friedrich, M., Cermak, T., Maderbacher, P.: The effect of brochure use versus therapist teaching on patients performing therapeutic exercise and on changes in impairment status. Phys. Ther. 76, 1082–1088 (1996)

    Article  Google Scholar 

  15. Chonbodeechalermroong, A., Chalidabhongse, T.H.: Dynamic contour matching for hand gesture recognition from monocular image. In: 2015 12th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 47–51 (2015)

    Google Scholar 

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Correspondence to Chotirat Ann Ratanamahatana .

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Chonbodeechalermroong, A., Ratanamahatana, C.A. (2018). Robust Scale-Invariant Normalization and Similarity Measurement for Time Series Data. In: Sieminski, A., Kozierkiewicz, A., Nunez, M., Ha, Q. (eds) Modern Approaches for Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 769. Springer, Cham. https://doi.org/10.1007/978-3-319-76081-0_13

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  • DOI: https://doi.org/10.1007/978-3-319-76081-0_13

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