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Discovering All-Chain Set in Streaming Time Series

  • Shaopeng WangEmail author
  • Ye Yuan
  • Hua Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11439)

Abstract

Time series chains discovery is an increasingly popular research area in time series mining. Previous studies on this topic process fixed-length time series. In this work, we focus on the issue of all-chain set mining over the streaming time series, where the all-chain set is a very important kind of the time series chains. We propose a novel all-chain set mining algorithm about streaming time series (ASMSTS) to solve this problem. The main idea behind the ASMSTS is to obtain the mining results at current time-tick based on the ones at the last one. This makes the method more efficiency in time and space than the Naïve. Our experiments illustrate that ASMSTS does indeed detect the all-chain set correctly and can offer dramatic improvements in speed and space cost over the Naive method.

Keywords

Streaming time series Time series chains All-chain set 

Notes

Acknowledgement

This research is supported by: the Natural Science Foundation of Inner Mongolia in China (Grant nos. 2018BS06001), the National Natural Science Foundation of China (Grant nos. 61862047, 61572119, 61622202), and the Fundamental Research Funds for the Central universities (Grant No.N150402005).

References

  1. 1.
    Begum, N., Keogh, E.: Rare time series motif discovery from unbounded streams. In: VLDB 2015, pp. 149–160. Association for Computing Machinery, USA (2015)Google Scholar
  2. 2.
    Patel, P., Keogh, E., Lin, J., Lonardi, S.: Mining motifs in massive time series databases. In: ICDM 2002, pp. 370–377. IEEE Computer Society, Piscataway (2002)Google Scholar
  3. 3.
    Zhu, Y., Zimmerman, Z., Senobari, N.S., et al.: Matrix profile II: exploiting a novel algorithm and GPUs to break the one hundred million barrier for time series motifs and joins. In: ICDM 2016, pp. 739–748. IEEE Computer Society, Piscataway (2016)Google Scholar
  4. 4.
    Yeh, C.C.M., Zhu, Y., Ulanova, L., et al.: Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In: ICDM 2016, pp. 1317–1322, IEEE Computer Society, Piscataway (2016)Google Scholar
  5. 5.
    Hao, M.C., Marwah, M., Janetzko, H., et al.: Visualexploration of frequent patterns in multivariate time series. Inf. Vis. 11(1), 71–83 (2012)CrossRefGoogle Scholar
  6. 6.
    Shokoohi-Yekta, M., Chen, Y.P., Campana, B., et al.: Discovery of meaningful rules in time series. In: Proceedings of the 21th ACM SIGKDD, Philadelphia, PA, USA, pp. 1085–1094 (2015)Google Scholar
  7. 7.
    Syed, Z., Stultz, C., Kellis, M., et al.: Motif discovery in physiological datasets: a methodology for inferring predictive elements. TKDD 4(1), 2 (2010)CrossRefGoogle Scholar
  8. 8.
    Zhu, X., Oates, T.: Finding story chains in newswire articles. In: Proceedings of the 2012 IEEE 13th International Conference on Information Reuse and Integration, pp. 93–100. IEEE Computer Society, Piscataway (2012)Google Scholar
  9. 9.
    Zhu, Y., Imamura, M., Nikovski, D.: Matrix profile VII: time series chains: a new primitive for time series data mining. In: ICDM 2017, pp. 695–704. IEEE, Computer Society, Piscataway (2017)Google Scholar
  10. 10.
    Zhu, Y., Imamura, M., Nikovski, D.: Introducing time series chains: a new primitive for time series data mining. Knowl. Inf. Syst. (2018).  https://doi.org/10.1007/s10115-018-1224-8
  11. 11.
    Zhu, Y., Imamura, M., Nikovski, D., Keogh, E.: Time series chain: A Novel tool for time series data mining. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence 2018, pp. 5414–5418, Springer Verlag, Heidelberg (2018)Google Scholar
  12. 12.
    Bögel, T., Gertz, M.: Time will tell: temporal linking of news stories. In: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries 2015, pp. 195–204. IEEE Computer Society, Piscataway (2015)Google Scholar
  13. 13.
    Li, Z., Han, J., Ding, B., Kays, R.: Mining periodic behaviors of object movements for animal and biological sustainability studies. Data Min. Knowl. Discov. 24(2), 355–386 (2012)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. (CSUR) 46(4), 1–37 (2014)CrossRefGoogle Scholar
  15. 15.
    Chiu, B., Keogh, E., Lonardi, S.: Probabilistic discovery of time series motifs. In: ACM SIGKDD 2003, Philadelphia, PA, USA, pp. 493–498 (2003)Google Scholar
  16. 16.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Inner Mongolia UniversityHohhotChina
  2. 2.Northeastern UniversityShenyangChina

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