Advertisement

Cluster Computing

, Volume 22, Supplement 3, pp 7313–7320 | Cite as

A novel time series behavior matching algorithm for online conversion algorithms

  • Iftikhar AhmadEmail author
  • Javeria Iqbal
Article
  • 221 Downloads

Abstract

This work presents a novel time series behavior matching algorithm for analyzing behavior (trend) similarity between two given time series. Unlike traditional approaches, our dynamic programming based approach “Behavior Matching (BM)” is based on trends and behavior rather than absolute distance as similarity measure. In order to compare the effectiveness of our proposed algorithm, we conduct an experimental study on real world stock data (DAX30). We compare our proposed algorithm with state-of-the-art algorithm Euclidean Distance, V-Shift and Dynamic Time Warping. The experimental results validates the performance guarantee and consistency of our proposed scheme.

Keywords

Time series matching Behavior analysis for online conversion Similarity measures 

Notes

Acknowledgements

We would like to thank the anonymous referees for their valuable input.

References

  1. 1.
    Mohr, E., Ahmad, I., Schmidt, G.: Online algorithms for conversion problems: a survey. Surv. Oper. Res. Manag. Sci. 19(2), 87–104 (2014)MathSciNetGoogle Scholar
  2. 2.
    Wang, X., Mueen, A., Ding, H., Trajcevski, G., Scheuermann, P., Keogh, E.: Experimental comparison of representation methods and distance measures for time series data. Data Min. Knowl. Discov. 26(2), 275–309 (2013)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Chan, K.P., Fu, A.W.: Efficient time series matching by wavelets. In: Proceedings of the 15th International Conference on Data Engineering, ICDE ’99, Washington, DC, USA. IEEE Computer Society (1999)Google Scholar
  4. 4.
    Keogh, E., Ratanamahatana, C.A.: Exact indexing of dynamic time warping. Knowl. Inf. Syst. 7, 358–386 (2005)CrossRefGoogle Scholar
  5. 5.
    Das, G., Gunopulos, D., Mannila, H.: Finding similar time series. In: Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery, PKDD ’97, pp. 88–100, Springer-Verlag, London, UK (1997)CrossRefGoogle Scholar
  6. 6.
    Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases. In: Proceedings of the 1994 ACM SIGMOD international conference on Management of data, SIGMOD ’94, ACM pp. 419–429, New York, NY, USA (1994)Google Scholar
  7. 7.
    Paparrizos, J., Gravano, L.: Fast and accurate time-series clustering. ACM Trans. Database Syst. 42(2), 8 (2017)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Tak-chung, F.: A review on time series data mining. Eng. Appl. Artif. Intell. 24(1), 164–181 (2011)CrossRefGoogle Scholar
  9. 9.
    Lhermitte, S., Verbesselt, J., Verstraeten, W.W., Coppin, P.: A comparison of time series similarity measures for classification and change detection of ecosystem dynamics. Remote Sens. Environ. 115(12), 3129–3152 (2011)CrossRefGoogle Scholar
  10. 10.
    Mondal, T., Ragot, N., Ramel, J., Palb, U.: Comparative study of conventional time series matching techniques for word spotting. Pattern Recognit. 73, 47–64 (2018)CrossRefGoogle Scholar
  11. 11.
    Wan, Y., Gong, X., Si, Y.W.: Effect of segmentation on financial time series pattern matching. Appl. Soft Comput. 38, 346–359 (2016)CrossRefGoogle Scholar
  12. 12.
    Vlachos, M., Gunopoulos, D., Kollios, G.: Discovering similar multidimensional trajectories. In: Proceedings of the 18th International Conference on Data Engineering, ICDE ’02, Washington, DC, USA. IEEE Computer Society (2002)Google Scholar
  13. 13.
    El-Yaniv, R., Fiat, A., Karp, R.M., Turpin, G.: Optimal search and one-way trading algorithm. Algorithmica 30, 101–139 (2001)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Computer Science & Information TechnologyUniversity of Engineering & TechnologyPeshawarPakistan
  2. 2.Punjab University College of Information Technology, University of PunjabLahorePakistan

Personalised recommendations