Advertisement

An Altered Kernel Transformation for Time Series Classification

  • Yangtao Xue
  • Li ZhangEmail author
  • Zhiwei Tao
  • Bangjun Wang
  • Fanzhang Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10638)

Abstract

Motivated by the great efficiency of dynamic time warping (DTW) for time series similarity measure, a Gaussian DTW (GDTW) kernel has been developed for time series classification. This paper proposes an altered Gaussian DTW (AGDTW) kernel function, which takes into consideration each of warping path between time series. Time series can be mapped into a special kernel space where the homogeneous data gather together and the heterogeneous data separate from each other. Classification results on transformed time series combined with different classifiers demonstrate that the AGDTW kernel is more powerful to represent and classify time series than the Gaussian radius basis function (RBF) and GDTW kernels.

Keywords

Dynamic time warping Gaussian dynamic time warping kernel Time series classification Gaussian radius basis function kernel Warping path 

Notes

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61373093 and 61672364, by the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20140008, and by the Soochow Scholar Project.

References

  1. 1.
    Chen, Z., Zuo, W., Hu, Q., Lin, L.: Kernel sparse representation for time series classification. Inf. Sci. 292, 15–26 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Kai, N., Kortelainen, J., Seppänen, T.: Invariant trajectory classification of dynamical systems with a case study on ECG. Pattern Recogn. 42(9), 1832–1844 (2009)CrossRefzbMATHGoogle Scholar
  3. 3.
    Lichtenauer, J.F., Hendriks, E.A., Reinders, M.J.: Sign language recognition by combining statistical DTW and independent classification. IEEE Trans. Pattern Anal. Mach. Intell. 30(11), 2040–2046 (2008)CrossRefGoogle Scholar
  4. 4.
    Fu, T.C., Law, C.W., Chan, K.K., Chung, F.L., Ng, C.M.: Stock time series categorization and clustering via SB-tree optimization. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds.) FSKD 2006. LNCS, vol. 4223, pp. 1130–1139. Springer, Heidelberg (2006). doi: 10.1007/11881599_141 CrossRefGoogle Scholar
  5. 5.
    Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases. In: ACM SIGMOD International Conference on Management of Data, vol. 23, pp. 419–429. ACM (2001)Google Scholar
  6. 6.
    Li, H.L., Guo, C.H.: Piecewise cloud approximation for time series mining. Control Decis. 26(10), 1525–1529 (2011)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Li, H., Guo, C., Qiu, W.: Similarity measure based on piecewise linear approximation and derivative dynamic time warping for time series mining. Expert Syst. Appl. 38, 14732–14743 (2011). Pergamon Press, Inc.CrossRefGoogle Scholar
  8. 8.
    Zhang, L., Tao, Z.: Time series classification based on multi-codebook piecewise vector quantized approximation. In: IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 385–390 (2015)Google Scholar
  9. 9.
    Cortes, C., Vapnik, V.: Support vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  10. 10.
    Zhang, D., Zuo, W., Zhang, D., Zhang, H.: Time series classification using support vector machine with Gaussian elastic metric kernel. In: IEEE International Conference on Pattern Recognition, pp. 29–32 (2010)Google Scholar
  11. 11.
    Zhang, L., Zhou, W.D., Chang, P.C., Liu, J., Yan, Z., Wang, T., Li, F.Z.: Kernel sparse representation-based classifier. IEEE Trans. Signal Process. 60(4), 1684–1695 (2012)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Gao, S., Tsang, I.W., Ma, Y.: Learning category-specific dictionary and shared dictionary for fine-grained image categorization. IEEE Trans. Image Process. 23(2), 623–634 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: KDD workshop, vol. 10, pp. 359–370 (1994)Google Scholar
  14. 14.
    Vlachos, M., Hadjieleftheriou, M., Gunopulos, D., Keogh, E.: Indexing multi-dimensional time-series with support for multiple distance measures. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 216–225, ACM (2003)Google Scholar
  15. 15.
    Chen, L., Ng, R.: On the marriage of lp-norms and edit distance. In: Proceedings of the Thirtieth International Conference on Very Large Data Bases, vol. 30, pp. 792–803. VLDB Endowment (2004)Google Scholar
  16. 16.
    Marteau, P.F.: Time warp edit distance with stiffness adjustment for time series matching. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 306–318 (2009)CrossRefGoogle Scholar
  17. 17.
    Pree, H., Herwig, B., Gruber, T., Sick, B., David, K., Lukowicz, P.: On general purpose time series similarity measures and their use as kernel functions in support vector machines. Inf. Sci. 281(4), 478–495 (2014)CrossRefGoogle Scholar
  18. 18.
    Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yangtao Xue
    • 1
  • Li Zhang
    • 1
    Email author
  • Zhiwei Tao
    • 1
  • Bangjun Wang
    • 1
  • Fanzhang Li
    • 1
  1. 1.School of Computer Science and Technology & Joint International Research Laboratory of Machine Learning and Neuromorphic ComputingSoochow UniversitySuzhouChina

Personalised recommendations