Advertisement

A Continuous Segmentation Algorithm for Streaming Time Series

  • Yupeng Hu
  • Cun Ji
  • Ming Jing
  • Yiming Ding
  • Shuo Kuai
  • Xueqing LiEmail author
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 201)

Abstract

Along with the arrival of Industry 4.0 era, massive numbers of detecting instruments in various fields are continuously producing a plenty number of time series stream data. In order to efficiently and effectively analyze and mine the high-dimensional streaming time series, the segmentation which provides more accurate representation to the raw time series data, should be done as the first step. In this paper, we propose a novel online segmentation approach based on the turning points to partition the time series into some continuous subsequences and maintain a high similarity between the processed subsequences and the raw data. It achieves the best overall performance on the segmentation results compared with other baseline methods. Extensive experiments on all kinds of typical time series datasets have been conducted to demonstrate the advantages of our method.

Keywords

Data mining Time series Online segmentation Algorithms 

Notes

Acknowledgment

The authors would like to acknowledge the support provided by the Novel Software Technology Project(KFKT2015B02) and the Science & Technology Development Project of Shandong Province (2015GGX101009).

References

  1. 1.
    Lin, J., Keogh, E., Lonardi, S.: A symbolic representation of time series, with implications for streaming algorithms. In: Proceedings of ACM SIGMOD (2003)Google Scholar
  2. 2.
    Chandra, R.: Competition and collaboration in cooperative coevolution of Elman recurrent neural networks for time-series prediction. IEEE Trans. Neural Netw. Learn. Syst. 26(12), 3123–3136 (2015)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Jamali, S., Jönsson, P., Eklundh, L., Ardö, J., Seaquist, J.: Detecting changes in vegetation trends using time series segmentation. Remote Sens. Environ. 156, 182–195 (2015)CrossRefGoogle Scholar
  4. 4.
    Palpanas, T., Vlachos, M., Keogh, E.: Online amnesic approximation of streaming time series. In: Proceedings of IEEE ICDE 2004 (2004)Google Scholar
  5. 5.
    Luo, G., Yi, K., Cheng, S.W., Li, Z., Fan, W., He, C., Mu, Y.: Piecewise linear approximation of streaming time series data with max-error guarantees. In Proceedings of IEEE ICDE 2015(2015)Google Scholar
  6. 6.
    Chiu, B., Keogh, E., Lonardi, S.: Probabilistic discovery of time series motifs proceedings. In: Proceedings of ACM SIGKDD 2003 (2003)Google Scholar
  7. 7.
    Lin, J., Keogh, E., Patel, P., Lonardi, S.: Finding motifs in time series. In: Proceedings of ACM SIGKDD 2002 (2002)Google Scholar
  8. 8.
    Fayyad, U., Reina, C., Bradley, P.: Initialization of iterative refinement clustering algorithms. In: Proceedings of ACM SIGKDD 1998 (1998)Google Scholar
  9. 9.
    Yi, B., Faloutsos, B.: Fast time sequence indexing for arbitrary Lp-norms. In: Proceedings of VLDB International Conference 2000 (2000)Google Scholar
  10. 10.
    Lazaridis, I., Mehrotra, S.: Capturing sensor-generated time series with quality guarantees. In: Proceedings of IEEE ICDE 2003 (2003)Google Scholar
  11. 11.
    Wang, C., Wang, S.: Supporting content-based searches on time Series via approximation. In: Proceedings of IEEE SSDBM 2000 (2000)Google Scholar
  12. 12.
    Keogh, E., Chu, S., Hart, D., Pazzani, M.: An online algorithm for segmenting time series. In: Proceedings of IEEE ICDM 2001 (2001)Google Scholar
  13. 13.
    Liu, X., Lin, X., Wang, H.: Novel online methods for time series segmentation. TKDE 20(12), 1616–1626 (2008)Google Scholar
  14. 14.
    Shatkay, H., Zdonik, S.B.: Approximate queries and representations for large data sequences. In: Proceedings of IEEE ICDE 1996 (1996)Google Scholar
  15. 15.
    Chen, Y., Nascimento, M.A., Ooi, B.C., Tung, A.K.H.: SpADe: on shape-based pattern detection in streaming time series. In: Proceedings of IEEE ICDE 2007 (2007)Google Scholar
  16. 16.
    Li, Q., Lopez, I.F.V., Moon, B.: Skyline index for time series data. IEEE Trans. Knowl. Data Eng. 16(6), 669–684 (2004)CrossRefGoogle Scholar
  17. 17.
    Zhou, D.: Time Series Segmentation Based on Series Importance Point. Computer Engineering, 2008(34) (2008)Google Scholar
  18. 18.
    Ji, C., Liu, S., et al.: A piecewise linear representation method based on importance data points for time series data. In: Proceedings of IEEE CSCWD 2016 (2016)Google Scholar
  19. 19.
    Keogh, E., et al.: Fast similarity search in the presence of longitudinal scaling in time series databases. In: Proceedings of IEEE ICTAI 1997 (1997)Google Scholar
  20. 20.
    Yin, J., Si, Y., Gong, Z.: Financial time series segmentation based on turning points. In: IEEE ICSSE 2011 (2011)Google Scholar
  21. 21.
    Keogh, E., Folias, T.: The UCR Time Series Data Mining Archive, Computer Science and Engineering Department, University of California (2002). www.cs.ucr.edu/~eamonn/TSDMA/index.html

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  • Yupeng Hu
    • 1
    • 2
  • Cun Ji
    • 1
  • Ming Jing
    • 1
  • Yiming Ding
    • 1
  • Shuo Kuai
    • 1
  • Xueqing Li
    • 1
    Email author
  1. 1.School of Computer Science and TechnologyShandong UniversityJinanChina
  2. 2.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

Personalised recommendations