Improving Time-Series Rule Matching Performance for Detecting Energy Consumption Patterns

  • Maël Guillemé
  • Laurence Rozé
  • Véronique Masson
  • Cérès Carton
  • René Quiniou
  • Alexandre Termier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10691)

Abstract

More and more sensors are used in industrial systems (machines, plants, factories...) to capture energy consumption. All these sensors produce time series data. Abnormal behaviours leading to over-consumption can be detected by experts and represented by sub-sequences in time series, which are patterns. Predictive time series rules are used to detect new occurrences of these patterns as soon as possible.

Standard rule discovery algorithms discretize the time series to perform symbolic rule discovery. The discretization requires fine tuning (dilemma between accuracy and understandability of the rules). The first promising proposal of rule discovery algorithm was proposed by Shokoohi et al., which extracts predictive rules from non-discretized data. An important feature of this algorithm is the distance used to compare two sub-sequences in a time series. Shokoohi et al. propose to use the Euclidean distance to search candidate rules occurrences. However this distance is not adapted for energy consumption data because occurrences of patterns should have different duration. We propose to use more “elastic” distance measures. In this paper we will compare the detection performance of predictive rules based on several variations of Dynamic Time Warping (DTW) and show the superiority of subsequenceDTW.

References

  1. 1.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: ACM SIGMOD Record, vol. 22, pp. 207–216. ACM (1993)Google Scholar
  2. 2.
    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
  3. 3.
    Das, G., Lin, K., Mannila, H., Renganathan, G., Smyth, P.: Rule discovery from time series. In: KDD 1998, pp. 16–22 (1998)Google Scholar
  4. 4.
    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 Endow. 1(2), 1542–1552 (2008)CrossRefGoogle Scholar
  5. 5.
    Esling, P., Agon, C.: Time-series data mining. ACM Comput. Surv. (CSUR) 45(1), 12 (2012)CrossRefMATHGoogle Scholar
  6. 6.
    Harms, S.K., Deogun, J., Tadesse, T.: Discovering sequential association rules with constraints and time lags in multiple sequences. In: Hacid, M.-S., Raś, Z.W., Zighed, D.A., Kodratoff, Y. (eds.) ISMIS 2002. LNCS (LNAI), vol. 2366, pp. 432–441. Springer, Heidelberg (2002).  https://doi.org/10.1007/3-540-48050-1_47 CrossRefGoogle Scholar
  7. 7.
    Hetland, M.L., Sætrom, P.: Temporal rule discovery using genetic programming and specialized hardware. In: Lotfi, A., Garibaldi, J.M. (eds.) Applications and Science in Soft Computing. Advances in Soft Computing, vol. 24, pp. 87–94. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-45240-9_13
  8. 8.
    Jin, X., Lu, Y., Shi, C.: Distribution discovery: local analysis of temporal rules. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS (LNAI), vol. 2336, pp. 469–480. Springer, Heidelberg (2002).  https://doi.org/10.1007/3-540-47887-6_47 CrossRefGoogle Scholar
  9. 9.
    Keogh, E., Lin, J., Truppel, W.: Clustering of time series subsequences is meaningless: implications for previous and future research. In: Third IEEE International Conference on Data Mining, ICDM 2003, pp. 115–122. IEEE (2003)Google Scholar
  10. 10.
    Lin, J., Keogh, E., Lonardi, S., Patel, P.: Finding motifs in time series. In: Proceedings of the 2nd Workshop on Temporal Data Mining, pp. 53–68 (2002)Google Scholar
  11. 11.
    Müller, M.: Information Retrieval for Music and Motion, vol. 2. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Rissanen, J.: Modeling by shortest data description. Automatica 14(5), 465–471 (1978)CrossRefMATHGoogle Scholar
  13. 13.
    Salvador, S., Chan, P.: Toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11(5), 561–580 (2007)Google Scholar
  14. 14.
    Sang Hyun, P., Wesley, W., et al.: Discovering and matching elastic rules from sequence databases. Fundamenta Informaticae 47(1–2), 75–90 (2001)MATHMathSciNetGoogle Scholar
  15. 15.
    Shokoohi-Yekta, M., Chen, Y., Campana, B., Hu, B., Zakaria, J., Keogh, E.: Discovery of meaningful rules in time series. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1085–1094. ACM (2015)Google Scholar
  16. 16.
    Shokoohi-Yekta, M., Hu, B., Jin, H., Wang, J., Keogh, E.: Generalizing dtw to the multi-dimensional case requires an adaptive approach. Data Min. Knowl. Disc. 31(1), 1–31 (2017)CrossRefMathSciNetGoogle Scholar
  17. 17.
    Silva, D.F., Batista, G.E.A.P.A., Keogh, E., et al.: On the effect of endpoints on dynamic time warping. In: SIGKDD Workshop on Mining and Learning from Time Series, II. Association for Computing Machinery-ACM (2016)Google Scholar
  18. 18.
    Tormene, P., Giorgino, T., Quaglini, S., Stefanelli, M.: Matching incomplete time series with dynamic time warping: an algorithm and an application to post-stroke rehabilitation. Artif. Intell. Med. 45(1), 11–34 (2009)CrossRefGoogle Scholar
  19. 19.
    Wu, H., Salzberg, B., Zhang, D.: Online event-driven subsequence matching over financial data streams. In: Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, pp. 23–34. ACM (2004)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Maël Guillemé
    • 1
    • 2
  • Laurence Rozé
    • 1
  • Véronique Masson
    • 1
  • Cérès Carton
    • 2
  • René Quiniou
    • 1
  • Alexandre Termier
    • 1
  1. 1.INSA, INRIA/IRISA, Université Rennes 1RennesFrance
  2. 2.EnergiencyRennesFrance

Personalised recommendations