Multivariate Time Series Classification by Combining Trend-Based and Value-Based Approximations

  • Bilal Esmael
  • Arghad Arnaout
  • Rudolf K. Fruhwirth
  • Gerhard Thonhauser
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7336)


Multivariate time series data often have a very high dimensionality. Classifying such high dimensional data poses a challenge because a vast number of features can be extracted. Furthermore, the meaning of the normally intuitive term “similar to” needs to be precisely defined. Representing the time series data effectively is an essential task for decision-making activities such as prediction, clustering and classification. In this paper we propose a feature-based classification approach to classify real-world multivariate time series generated by drilling rig sensors in the oil and gas industry. Our approach encompasses two main phases: representation and classification.

For the representation phase, we propose a novel representation of time series which combines trend-based and value-based approximations (we abbreviate it as TVA). It produces a compact representation of the time series which consists of symbolic strings that represent the trends and the values of each variable in the series. The TVA representation improves both the accuracy and the running time of the classification process by extracting a set of informative features suitable for common classifiers.

For the classification phase, we propose a memory-based classifier which takes into account the antecedent results of the classification process. The inputs of the proposed classifier are the TVA features computed from the current segment, as well as the predicted class of the previous segment.

Our experimental results on real-world multivariate time series show that our approach enables highly accurate and fast classification of multivariate time series.


Time Series Classification Time Series Representation Symbolic Aggregate Approximation Event Detection 


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  1. 1.
    Ratanamahatana, C.A., Lin, J., Gunopulos, D., Keogh, E., Vlachos, M., Das, G.: In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook 2010, 2nd edn., pp. 1049–1077. Springer (2010)Google Scholar
  2. 2.
    Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A Symbolic Representation of Time Series, with Implications for Streaming Algorithms. In: Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, San Diego, CA, June 13 (2003)Google Scholar
  3. 3.
    Batal, I., Valizadegan, H., Cooper, G.F., Hauskrecht, M.: A Pattern Mining Approach for Classifying Multivariate Temporal Data. In: IEEE International Conference on Bioinformatics and Biomedicine, Atlanta, Georgia (November 2011)Google Scholar
  4. 4.
    Batal, I., Sacchi, L., Bellazzi, R., Hauskrecht, M.: Multivariate Time Series Classification with Temporal Abstractions. In: Proceedings of the Twenty-Second International Florida AI Research Society Conference (FLAIRS 2009) (May 2009) Google Scholar
  5. 5.
    Onishi, A., Watanabe, C.: Event Detection using Archived Smart House Sensor Data obtained using Symbolic Aggregate Approximation. In: PDPTA (2011) Google Scholar
  6. 6.
    Zoumboulakis, M., Roussos, G.: Escalation: Complex Event Detection in Wireless Sensor Networks. In: Kortuem, G., Finney, J., Lea, R., Sundramoorthy, V. (eds.) EuroSSC 2007. LNCS, vol. 4793, pp. 270–285. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Wei, L., Keogh, E.: Semi-Supervised Time Series Classification. In: The Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, SIGKDD (2006) Google Scholar
  8. 8.
    Keogh, E., Pazzani, M.: An enhanced representation of time series which allows fast and accurate classification clustering and relevance feedback. In: 4th International Conference on Knowledge Discovery and Data Mining, New York, August 27-31, pp. 239–243 (1998)Google Scholar
  9. 9.
    Hung, N.Q.V., Anh, D.T.: Combining SAX and Piecewise Linear Approximation to Improve Similarity Search on Financial Time Series. In: Proceedings of the 2007 IEEE International Symposium on Information Technology Convergence (ISITC 2007), Jeonju, Korea (2007) Google Scholar
  10. 10.
    Kontaki, M., Papadopoulos, A.N., Manolopoulos, Y.: Continuous Trend-Based Classification of Streaming Time Series. In: Eder, J., Haav, H.-M., Kalja, A., Penjam, J. (eds.) ADBIS 2005. LNCS, vol. 3631, pp. 294–308. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Keogh, E., Lin, J., Fu, A.: HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence. In: Proceeding of the 5th IEEE International Conference on Data Mining (ICDM 2005), Houston, Texas, November 27-30, pp. 226–233 (2005)Google Scholar
  12. 12.
    Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: YALE: Rapid Prototyping for Complex Data Mining Tasks. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2006 (2006)Google Scholar
  13. 13.
    Chih-Chung, C., Chih-Jen, L.: LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), Software, Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Bilal Esmael
    • 1
  • Arghad Arnaout
    • 2
  • Rudolf K. Fruhwirth
    • 2
  • Gerhard Thonhauser
    • 1
  1. 1.University of LeobenLeobenAustria
  2. 2.TDE GmbHLeobenAustria

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