Skip to main content
SpringerLink
Log in

Early classification on time series

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

In this paper, we formulate the problem of early classification of time series data, which is important in some time-sensitive applications such as health informatics. We introduce a novel concept of MPL (minimum prediction length) and develop ECTS (early classification on time series), an effective 1-nearest neighbor classification method. ECTS makes early predictions and at the same time retains the accuracy comparable with that of a 1NN classifier using the full-length time series. Our empirical study using benchmark time series data sets shows that ECTS works well on the real data sets where 1NN classification is effective.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Bernaille L, Teixeira R, Akodkenou I, Soule A, Salamatian K (2006) Traffic classification on the fly. SIGCOMM Comput Commun Rev 36: 23–26

    Article  Google Scholar 

  2. Bregon A, Simon MA, Rodriguez JJ, Alonso C, Pulido B, Moro I (2006) Early fault classification in dynamic systems using case-based reasoning. In: Current topics in artificial intelligence, vol 4177 of lecture notes in computer science. Springer, Berlin, pp 211–220

  3. Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inform Theor 13(1): 21–27

    Article  MATH  Google Scholar 

  4. Ding CHQ, He X (2005) Cluster aggregate inequality and multi-level hierarchical clustering. In: PKDD’05: proceedings of the 9th European conference on principles and practice of knowledge discovery in databases, pp 71–83

  5. Ding C, He X (2004) K-nearest-neighbor consistency in data clustering: incorporating local information into global optimization. In: SAC’04: proceedings of the 2004 ACM symposium on applied computing, ACM, New York, NY, USA, pp 584–589

  6. Ding H, Trajcevski G, Scheuermann P, Wang X, Keogh E (2008) Querying and mining of time series data: experimental comparison of representations and distance measures. Proc VLDB Endow 1(2): 1542–1552

    Google Scholar 

  7. Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1): 119–139

    Article  MathSciNet  MATH  Google Scholar 

  8. Griffin MP, Moorman JR (2001) Toward the early diagnosis of neonatal sepsis and sepsis-like illness using novel heart rate analysis. Pediatrics 107(1): 97–104

    Article  Google Scholar 

  9. Ji X, Bailey J, Dong G (2007) Mining minimal distinguishing subsequence patterns with gap constraints. Knowl Inf Syst 11(3): 259–286

    Article  Google Scholar 

  10. Kadous MW, Sammut C (2005) Classification of multivariate time series and structured data using constructive induction. Mach Learn 58(2–3): 179–216

    Article  Google Scholar 

  11. Keogh E, Kasetty S (2002) On the need for time series data mining benchmarks: a survey and empirical demonstration. In: KDD’02: proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, New York, NY, USA, pp 102–111

  12. Keogh E, Xi X, Wei L, Ratanamahatana CA (2006) The UCR time series classification and clustering homepage:http://www.cs.ucr.edu/~eamonn/time_series_data/

  13. Lesh N, Zaki MJ, Ogihara M (1999) Mining features for sequence classification. In: KDD’99: proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, New York, NY, USA, pp 342–346

  14. Lin J, Keogh E, Wei L, Lonardi S (2007) Experiencing sax: a novel symbolic representation of time series. Data Min Knowl Discov 15(2): 107–144

    Article  MathSciNet  Google Scholar 

  15. Rodríguez JJ, Alonso CJ, Boström H (2001) Boosting interval based literals. Intell Data Anal 5: 245–262

    MATH  Google Scholar 

  16. Wei L, Keogh E (2006) Semi-supervised time series classification. In: KDD’06: proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, New York, NY, USA, pp 748–753

  17. Wei L, Keogh E, Van Herle H, Mafra-Neto A, Abbott RJ (2007) Efficient query filtering for streaming time series with applications to semisupervised learning of time series classifiers. Knowl Inf Syst 11(3): 313–344

    Article  Google Scholar 

  18. Xi X, Keogh E, Shelton C, Wei L, Ratanamahatana CA (2006) Fast time series classification using numerosity reduction. In: ICML’06: proceedings of the 23rd international conference on Machine learning. ACM, New York, NY, USA, pp 1033–1040

  19. Xing Z, Pei J, Dong G, Yu PS (2008) Mining sequence classifiers for early prediction. In: SDM’08: proceedings of the 2008 SIAM international conference on data mining, pp 644–655

  20. Xing Z, Pei J, Yu PS (2009) Early prediction on time series: a nearest neighbor approach. In: ‘Proceedings of the 21st international jont conference on Artifical intelligence’, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 1297–1302

  21. Xing Z, Pei J, Yu PS, Wang K (2011) Extracting interpretable features for early classification on time series. In: SDM’11: proceedings of the 2011 SIAM international conference on data mining

  22. Ye L, Keogh E (2009) Time series shapelets: a new primitive for data mining. In: proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, KDD’09. ACM, New York, NY, USA, pp 947–956

Download references

Author information

Authors and Affiliations

  1. Amazon.com Inc., Seattle, WA, USA

    Zhengzheng Xing

  2. School of Computing Science, Simon Fraser University, Burnaby, BC, Canada

    Jian Pei

  3. Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA

    Philip S. Yu

Authors
  1. Zhengzheng Xing
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jian Pei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Philip S. Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jian Pei.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Xing, Z., Pei, J. & Yu, P.S. Early classification on time series. Knowl Inf Syst 31, 105–127 (2012). https://doi.org/10.1007/s10115-011-0400-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-011-0400-x

Keywords

Search

Navigation