Advertisement

A 2D Transform Based Distance Function for Time Series Classification

  • Cun JiEmail author
  • Xiunan Zou
  • Yupeng HuEmail author
  • Shijun LiuEmail author
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 268)

Abstract

Along with the arrival of Industry 4.0 era, time series classification (TSC) has attracted a lot of attention in the last decade. The high dimensionality, high feature correlation and typically high levels of noise that found in time series bring great challenges to TSC. Among TSC algorithms, the 1NN classifier has been shown as effective and difficult to beat. The core of the 1NN classifier is the distance function. The large majority of TSC have concentrated on alternative distance functions. In this paper, a two-dimensional (2D) transform based distance (2DTbD) function is proposed. There are three steps in 2DTbD. Firstly, we convert time series to 2D space by turning time series around the coordinate origin. Then we calculate distances of each dimension. Finally, we ensemble distances in 2D space to get the final time series distance. Our distance function raises the accuracy rate through the fusion of 2D information. Experimental results demonstrate that the classification accuracy can be improved by 2DTbD.

Keywords

Internet of Things Data mining Time series classification 2D transform Distance function 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (61872222, 91546203), the National Key Research and Development Program of China (2017YFA0700601), the Major Program of Shandong Province Natural Science Foundation (ZR2018ZB0419), the Key Research and Development Program of Shandong Province (2017CXGC0605, 2017CXGC0604, 2018GGX101019).

References

  1. 1.
    Bagnall, A., Bostrom, A., Large, J., Lines, J.: The great time series classification bake off: an experimental evaluation of recently proposed algorithms. Extended version. arXiv preprint arXiv:1602.01711 (2016)
  2. 2.
    Bagnall, A., Bostrom, A., Lines, J.: The UEA TSC codebase (2016). https://bitbucket.org/aaron_bostrom/time-series-classification
  3. 3.
    Bagnall, A., Lines, J., Bostrom, A., Large, J., Keogh, E.: The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Disc. 31(3), 606–660 (2017)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Batista, G.E., Keogh, E.J., Tataw, O.M., De Souza, V.M.: CID: an efficient complexity-invariant distance for time series. Data Min. Knowl. Disc. 28(3), 634–669 (2014)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Batista, G.E., Wang, X., Keogh, E.J.: A complexity-invariant distance measure for time series. In: Proceedings of the 2011 SIAM International Conference on Data Mining, pp. 699–710. SIAM (2011)Google Scholar
  6. 6.
    Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: KDD Workshop, Seattle, WA, vol. 10, pp. 359–370 (1994)Google Scholar
  7. 7.
    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
  8. 8.
    Chen, Y., Hu, B., Keogh, E., Batista, G.E.: DTW-D: time series semi-supervised learning from a single example. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 383–391. ACM (2013)Google Scholar
  9. 9.
    Chen, Y., et al.: The UCR time series classification archive (2015). http://www.cs.ucr.edu/~eamonn/time_series_data
  10. 10.
    Chhieng, V.M., Wong, R.K.: Adaptive distance measurement for time series databases. In: Kotagiri, R., Krishna, P.R., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 598–610. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-71703-4_51CrossRefGoogle Scholar
  11. 11.
    Das, G., Gunopulos, D., Mannila, H.: Finding similar time series. In: Komorowski, J., Zytkow, J. (eds.) PKDD 1997. LNCS, vol. 1263, pp. 88–100. Springer, Heidelberg (1997).  https://doi.org/10.1007/3-540-63223-9_109CrossRefGoogle Scholar
  12. 12.
    Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., Keogh, E.: Querying and mining of time series data: experimental comparison of representations and distance measures. Proc. VLDB Endowment 1(2), 1542–1552 (2008)CrossRefGoogle Scholar
  13. 13.
    Esling, P., Agon, C.: Time-series data mining. ACM Comput. Surv. 45(1), 12 (2012)zbMATHCrossRefGoogle Scholar
  14. 14.
    Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases, vol. 23. ACM (1994)Google Scholar
  15. 15.
    Fu, T.: A review on time series data mining. Eng. Appl. Artif. Intell. 24(1), 164–181 (2011)CrossRefGoogle Scholar
  16. 16.
    Górecki, T., Łuczak, M.: Using derivatives in time series classification. Data Min. Knowl. Disc. 26(2), 310–331 (2013)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Górecki, T., Łuczak, M.: Non-isometric transforms in time series classification using DTW. Knowl.-Based Syst. 61, 98–108 (2014)zbMATHCrossRefGoogle Scholar
  18. 18.
    Itakura, F.: Minimum prediction residual principle applied to speech recognition. IEEE Trans. Acoust. Speech Signal Process. 23(1), 67–72 (1975)CrossRefGoogle Scholar
  19. 19.
    Jeong, Y.S., Jeong, M.K., Omitaomu, O.A.: Weighted dynamic time warping for time series classification. Pattern Recognit. 44(9), 2231–2240 (2011)CrossRefGoogle Scholar
  20. 20.
    Ji, C., et al.: A self-evolving method of data model for cloud-based machine data ingestion. In: 2016 IEEE 9th International Conference on Cloud Computing, pp. 814–819. IEEE (2016)Google Scholar
  21. 21.
    Ji, C., Liu, S., Yang, C., Pan, L., Wu, L., Meng, X.: A shapelet selection algorithm for time series classification: new directions. Procedia Comput. Sci. 129, 461–467 (2018)CrossRefGoogle Scholar
  22. 22.
    Ji, C., Zhao, C., Pan, L., Liu, S., Yang, C., Wu, L.: A fast shapelet discovery algorithm based on important data points. Int. J. Web Serv. Res. 14(2), 67–80 (2017)CrossRefGoogle Scholar
  23. 23.
    Keogh, E., Kasetty, S.: On the need for time series data mining benchmarks: a survey and empirical demonstration. Data Min. Knowl. Disc. 7(4), 349–371 (2003)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Keogh, E., Ratanamahatana, C.A.: Exact indexing of dynamic time warping. Knowl. Inf. Syst. 7(3), 358–386 (2005)CrossRefGoogle Scholar
  25. 25.
    Keogh, E., Wei, L., Xi, X., Lee, S.H., Vlachos, M.: LB\(\_\)Keogh supports exact indexing of shapes under rotation invariance with arbitrary representations and distance measures. In: Proceedings of the 32nd International Conference on Very Large Data Bases, pp. 882–893. VLDB Endowment (2006)Google Scholar
  26. 26.
    Kim, S.W., Park, S., Chu, W.W.: An index-based approach for similarity search supporting time warping in large sequence databases. In: Proceedings of 17th International Conference on Data Engineering, pp. 607–614. IEEE (2001)Google Scholar
  27. 27.
    Li, D., Bissyandé, T.F., Klein, J., Le Traon, Y.: DSCo-NG: a practical language modeling approach for time series classification. In: Boström, H., Knobbe, A., Soares, C., Papapetrou, P. (eds.) IDA 2016. LNCS, vol. 9897, pp. 1–13. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46349-0_1CrossRefGoogle Scholar
  28. 28.
    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
  29. 29.
    Prieto, O.J., Alonso-González, C.J., Rodríguez, J.J.: Stacking for multivariate time series classification. Pattern Anal. Appl. 18(2), 297–312 (2015)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Raza, A., Kramer, S.: Ensembles of randomized time series shapelets provide improved accuracy while reducing computational costs. arXiv preprint arXiv:1702.06712 (2017)
  31. 31.
    Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26(1), 43–49 (1978)zbMATHCrossRefGoogle Scholar
  32. 32.
    Sakurai, Y., Yoshikawa, M., Faloutsos, C.: FTW: fast similarity search under the time warping distance. In: Proceedings of the Twenty-Fourth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 326–337. ACM (2005)Google Scholar
  33. 33.
    Salvador, S., Chan, P.: Toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11(5), 561–580 (2007)CrossRefGoogle Scholar
  34. 34.
    Sampaio, A., Lima Jr., R.C., Mendonça, N.C., Filho, R.H.: Implementation and empirical assessment of a web application cloud deployment tool. Int. J. Cloud Comput. 1, 40–52 (2013). http://hipore.com/stcc/2013/IJCC-Vol1-No1-2013.pdf#page=46Google Scholar
  35. 35.
    Stefan, A., Athitsos, V., Das, G.: The move-split-merge metric for time series. IEEE Trans. Knowl. Data Eng. 25(6), 1425–1438 (2013)CrossRefGoogle Scholar
  36. 36.
    Wang, X., Mueen, A., Ding, H., Trajcevski, G., Scheuermann, P., Keogh, E.: Experimental comparison of representation methods and distance measures for time series data. Data Min. Knowl. Disc. 26(2), 1–35 (2013)MathSciNetCrossRefGoogle Scholar
  37. 37.
    Yi, B.K., Faloutsos, C.: Fast time sequence indexing for arbitrary Lp norms. VLDB (2000)Google Scholar
  38. 38.
    Yi, B.K., Jagadish, H., Faloutsos, C.: Efficient retrieval of similar time sequences under time warping. In: Proceedings of 14th International Conference on Data Engineering, pp. 201–208. IEEE (1998)Google Scholar
  39. 39.
    Zhang, Z., Cheng, J., Li, J., Bian, W., Tao, D.: Segment-based features for time series classification. Comput. J. 55(9), 1088–1102 (2012)CrossRefGoogle Scholar
  40. 40.
    Zhang, Z., Wen, Y., Zhang, Y., Yuan, X.: Time series classification by modeling the principal shapes. In: Bouguettaya, A., et al. (eds.) WISE 2017. LNCS, vol. 10569, pp. 406–421. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-68783-4_28CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.School of Information Science and EngineeringShandong Normal UniversityJinanChina
  2. 2.School of SoftwareShandong UniversityJinanChina
  3. 3.Engineering Research Center of Digital Media Technology, Ministry of EducationJinanChina
  4. 4.Shandong Provincial Key Laboratory of Software EngineeringShandong UniversityJinanChina

Personalised recommendations