Skip to main content
Springer Nature Link
Log in

Finding Structural Similarity in Time Series Data Using Bag-of-Patterns Representation

  • Conference paper
Scientific and Statistical Database Management (SSDBM 2009)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5566))

Abstract

For more than one decade, time series similarity search has been given a great deal of attention by data mining researchers. As a result, many time series representations and distance measures have been proposed. However, most existing work on time series similarity search focuses on finding shape-based similarity. While some of the existing approaches work well for short time series data, they typically fail to produce satisfactory results when the sequence is long. For long sequences, it is more appropriate to consider the similarity based on the higher-level structures. In this work, we present a histogram-based representation for time series data, similar to the “bag of words” approach that is widely accepted by the text mining and information retrieval communities. We show that our approach outperforms the existing methods in clustering, classification, and anomaly detection on several real datasets.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Agrawal, R., Faloutsos, C., Swami, A.: Efficient Similarity Search in Sequence Databases. In: Proceedings of the 4th Int’l Conference on Foundations of Data Organization and Algorithms, Chicago, IL, October 13-15, pp. 69–84 (1993)

    Google Scholar 

  2. Bradley, P., Fayyad, U., Reina, C.: Scaling Clustering Algorithms to Large Databases. In: Proceedings of the 4th Int’l Conference on Knowledge Discovery and Data Mining, New York, NY, August 27-31, pp. 9–15 (1998)

    Google Scholar 

  3. Chan, K., Fu, A.W.: Efficient Time Series Matching by Wavelets. In: Proceedings of the 15th IEEE Int’l Conference on Data Engineering, Sydney, Australia, March 23-26, pp. 126–133 (1999)

    Google Scholar 

  4. Deng, K., Moore, A., Nechyba, M.: Learning to Recognize Time Series: Combining ARMA models with Memory-based Learning. In: IEEE Int. Symp. on Computational Intelligence in Robotics and Automation, vol. 1, pp. 246–250 (1997)

    Google Scholar 

  5. Ekambaram, A., Montagne, E.: An Alternative Compressed Storage Format for Sparse Matrices. In: Yazıcı, A., Şener, C. (eds.) ISCIS 2003. LNCS, vol. 2869, pp. 196–203. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  6. Faloutsos, C., Ranganathan, M., Manolopulos, Y.: Fast Subsequence Matching in Time-Series Databases. SIGMOD Record 23, 419–429 (1994)

    Article  Google Scholar 

  7. Gavrilov, M., Anguelov, D., Indyk, P., Motwahl, R.: Mining the stock market: which measure is best? In: Proc. of the 6th ACM SIGKDD (2000)

    Google Scholar 

  8. Ge, X., Smyth, P.: Deformable Markov model templates for time-series pattern matching. In: Proceedings of the 6th ACM SIGKDD, Boston, MA, August 20-23, pp. 81–90 (2000)

    Google Scholar 

  9. Geurts, P.: Pattern Extraction for Time Series Classification. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS, vol. 2168, pp. 115–127. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  10. Goldberger, A.L., Amaral, L., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: Circulation. Discovery 101(23), 1(3), e215–e220 (1997)

    Google Scholar 

  11. Johnson, S.C.: Hierarchical Clustering Schemes. Psychometrika 2, 241–254 (1967)

    Article  Google Scholar 

  12. Keogh, E.: Exact indexing of dynamic time warping. In: Proceedings of the 28th international Conference on Very Large Data Bases, Hong Kong, China, August 20-23 (2002)

    Google Scholar 

  13. Keogh, E.: Tutorial in SIGKDD 2004. Data Mining and Machine Learning in Time Series Databases (2004)

    Google Scholar 

  14. Keogh, E., Folias, T.: The UCR Time Series Data Mining Archive. Riverside CA (2002), http://www.cs.ucr.edu/~eamonn/TSDMA/index.html

  15. Keogh, E., Kasetty, S.: On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery, Edmonton, Alberta, Canada, pp. 102–111 (2002)

    Google Scholar 

  16. Keogh, E., Lonardi, S., Ratanamahatana, C.A.: Towards parameter-free data mining. In: Proceedings of the Tenth ACM SIGKDD international Conference on Knowledge Discovery and Data Mining, KDD 2004, Seattle, WA, USA, August 22 - 25 (2004)

    Google Scholar 

  17. Keogh, E., Chakrabarti, K., Pazzani, M.: Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases. In: Proceedings of ACM SIGMOD Conference on Management of Data, Santa Barbara, May 21-24, pp. 151–162 (2001)

    Google Scholar 

  18. Keogh, E., Lin, J., Fu, A.: Finding the Most Unusual Time Series Subsequence: Algorithms and Applications. In: Knowledge and Information Systems (KAIS). Springer, Heidelberg (2006)

    Google Scholar 

  19. Li, M., Vitanyi, P.: An Introduction to Kolmogorov Complexity and Its Applications, 2nd edn. Springer, Heidelberg (1997)

    Book  MATH  Google Scholar 

  20. Lin, J., Keogh, E., Li, W., Lonardi, S.: Experiencing SAX: A Novel Symbolic Representation of Time Series. Data Mining and Knowledge Discovery Journal (2007)

    Google Scholar 

  21. Lin, J., Vlachos, M., Keogh, E., Gunopulos, D.: Iterative Incremental Clustering of Time Series. In: Bertino, E., Christodoulakis, S., Plexousakis, D., Christophides, V., Koubarakis, M., Böhm, K., Ferrari, E. (eds.) EDBT 2004. LNCS, vol. 2992, pp. 106–122. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  22. McQueen, J.: Some Methods for Classification and Analysis of Multivariate Observation. In: Le Cam, L., Neyman, J. (eds.) Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, CA, vol. 1, pp. 281–297 (1967)

    Google Scholar 

  23. Nanopoulos, A., Alcock, R., Manolopoulos, Y.: Feature-based classification of time-series data. In: Mastorakis, N., Nikolopoulos, S.D. (eds.) Information Processing and Technology, pp. 49–61. Nova Science Publishers, Commack (2001)

    Google Scholar 

  24. Ratanamahatana, C.A., Keogh, E.: Making Time-series Classification More Accurate Using Learned Constraints. In: Proceedings of SIAM International Conference on Data Mining (SDM 2004), Lake Buena Vista, Florida, April 22-24 (2004)

    Google Scholar 

  25. Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 19(11), 613–620 (1975)

    Article  MATH  Google Scholar 

  26. Wang, X., Smith, K., Hyndman, R.: Characteristic-Based Clustering for Time Series Data. Data Min. Knowl. Discov. 13(3), 335–364 (2006)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, George Mason University, USA

    Jessica Lin & Yuan Li

Authors
  1. Jessica Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Illinois at Urbana-Champaign, 201 N. Goodwin Avenue, IL 61801, Urbana, USA

    Marianne Winslett

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lin, J., Li, Y. (2009). Finding Structural Similarity in Time Series Data Using Bag-of-Patterns Representation. In: Winslett, M. (eds) Scientific and Statistical Database Management. SSDBM 2009. Lecture Notes in Computer Science, vol 5566. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02279-1_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-02279-1_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02278-4

  • Online ISBN: 978-3-642-02279-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics