Skip to main content
Log in

Fast classification of univariate and multivariate time series through shapelet discovery

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

Abstract

Time-series classification is an important problem for the data mining community due to the wide range of application domains involving time-series data. A recent paradigm, called shapelets, represents patterns that are highly predictive for the target variable. Shapelets are discovered by measuring the prediction accuracy of a set of potential (shapelet) candidates. The candidates typically consist of all the segments of a dataset; therefore, the discovery of shapelets is computationally expensive. This paper proposes a novel method that avoids measuring the prediction accuracy of similar candidates in Euclidean distance space, through an online clustering/pruning technique. In addition, our algorithm incorporates a supervised shapelet selection that filters out only those candidates that improve classification accuracy. Empirical evidence on 45 univariate datasets from the UCR collection demonstrates that our method is 3–4 orders of magnitudes faster than the fastest existing shapelet discovery method, while providing better prediction accuracy. In addition, we extended our method to multivariate time-series data. Runtime results over four real-life multivariate datasets indicate that our method can classify MB-scale data in a matter of seconds and GB-scale data in a matter of minutes. The achievements do not compromise quality; on the contrary, our method is even superior to the multivariate baseline in terms of classification accuracy.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. fs.ismll.de/publicspace/ScalableShapelets.

References

  1. Allan J, Papka R, Lavrenko V (1998) On-line new event detection and tracking. In: Proceedings of the 21st annual international ACM SIGIR conference on research and development in information retrieval (SIGIR ’98). ACM, New York, NY, USA, pp 37–45

  2. Banos O, Garcia R, Holgado-Terriza J, Damas M, Pomares H, Rojas I, Saez A, Villalonga C (2014) mhealthdroid: a novel framework for agile development of mobile health applications. In: Pecchia L, Chen L, Nugent C, Bravo J, (eds) Ambient assisted living and daily activities, vol 8868 of lecture notes in computer science. Springer, New York, pp 91–98

  3. Banos O, Toth MA, Damas M, Pomares H, Rojas I (2014) Dealing with the effects of sensor displacement in wearable activity recognition. Sensors 14(6):9995–10023

    Article  Google Scholar 

  4. Bruno B, Mastrogiovanni F, Sgorbissa A, Vernazza T, Zaccaria R (2013) Analysis of human behavior recognition algorithms based on acceleration data. In: IEEE international conference on robotics and automation (ICRA), pp 1602–1607

  5. Cetin MS, Mueen A, Calhoun VD (2015) Shapelet ensemble for multi-dimensional time series. In: SDM

  6. Chakrabarti K, Keogh E, Mehrotra S, Pazzani M (2002) Locally adaptive dimensionality reduction for indexing large time series databases. ACM Trans Database Syst 27(2):188–228

    Article  Google Scholar 

  7. Chang K-W, Deka B, Hwu W-M W, Roth D (2012) Efficient pattern-based time series classification on gpu. In: Proceedings of the 12th IEEE international conference on data mining

  8. Ghalwash M, Obradovic Z (2012) Early classification of multivariate temporal observations by extraction of interpretable shapelets. BMC Bioinform. doi:10.1186/1471-2105-13-195

  9. Grabocka J, Schilling N, Wistuba M, Schmidt-Thieme L (2014) Learning time-series shapelets. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining (KDD ’14). ACM, New York, NY, USA, pp 392–401. doi:10.1145/2623330.2623613

  10. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182

    MATH  Google Scholar 

  11. Hartmann B, Link N (2010) Gesture recognition with inertial sensors and optimized DTW prototypes. In: IEEE international conference on systems man and cybernetics

  12. Hartmann B, Schwab I, Link N (2010) Prototype optimization for temporarily and spatially distorted time series. In: The AAAI spring symposia

  13. He Q, Zhuang F, Shang T, Shi Z et al (2012) Fast time series classification based on infrequent shapelets. In: 11th IEEE international conference on machine learning and applications

  14. Hills J, Lines J, Baranauskas E, Mapp J, Bagnall A (2014) Classification of time series by shapelet transformation. Data Min Knowl Discov 28(4):851–881. doi:10.1007/s10618-013-0322-1

  15. Keogh E, Zhu Q, Hu B, Y, H, Xi X, Wei L, Ratanamahatana CA (2011) The UCR time series classification/clustering. www.cs.ucr.edu/~eamonn/time_series_data/

  16. Lines J, Bagnall A (2012) Alternative quality measures for time series shapelets. In: Yin, Hujun, Costa, José AF, Barreto, Guilherme (eds) Intelligent data engineering and automated learning. Lecture notes in computer science, vol 7435. Springer, Heidelberg pp 475–483

  17. Mueen A, Keogh E, Young N (2011) Logical-shapelets: an expressive primitive for time series classification. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining

  18. Rakthanmanon T, Keogh E (2013) Fast shapelets: a scalable algorithm for discovering time series shapelets. In: Proceedings of the 13th SIAM international conference on data mining

  19. Sivakumar P, Shajina T (2012) Human gait recognition and classification using time series shapelets. In: IEEE international conference on advances in computing and communications

  20. Williams B, Toussaint M, Storkey A (2006) Extracting motion primitives from natural handwriting data. In: Kollias S, Stafylopatis A, Duch W, Oja E (eds) Artificial neural networks ICANN 2006, vol 4132. Lecture notes in computer science. Springer, Berlin, pp 634–643

  21. Xing Z, Pei J, Yu P (2012) Early classification on time series. Knowl Inf Syst 31(1):105–127

    Article  Google Scholar 

  22. Xing Z, Pei J, Yu P, Wang K (2011) Extracting interpretable features for early classification on time series. In: Proceedings of the 11th SIAM international conference on data mining

  23. 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

  24. Ye L, Keogh E (2011) Time series shapelets: a novel technique that allows accurate, interpretable and fast classification. Data Min Knowl Discov 22(1):149–182

    Article  MathSciNet  MATH  Google Scholar 

  25. Zakaria J, Mueen A, Keogh E (2012) Clustering time series using unsupervised-shapelets. In: Proceedings of the 12th IEEE international conference on data mining

Download references

Acknowledgments

This study was partially co-funded by the Seventh Framework Programme (FP7) of the European Commission, through Project REDUCTION (www.reduction-project.eu) (# 288254).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Josif Grabocka.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Grabocka, J., Wistuba, M. & Schmidt-Thieme, L. Fast classification of univariate and multivariate time series through shapelet discovery. Knowl Inf Syst 49, 429–454 (2016). https://doi.org/10.1007/s10115-015-0905-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-015-0905-9

Keywords

Navigation