Advertisement

Frequent Temporal Inter-object Pattern Mining in Time Series

  • Nguyen Thanh VuEmail author
  • Vo Thi Ngoc Chau
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 244)

Abstract

Nowadays, time series is present in many various domains such as finance, medicine, geology, meteorology, etc. Mining time series for useful hidden knowledge is very significant in those domains to help users get fascinating insights into important temporal relationships of objects/phenomena along the time. Hence, in this paper, we introduce a notion of frequent temporal inter-object pattern and accordingly propose two frequent temporal pattern mining algorithms on a set of different time series. As compared to frequent sequential patterns, frequent temporal inter-object patterns are more informative with explicit and exact temporal information automatically discovered from many various time series. The two proposed algorithms which are brute-force and tree-based are efficiently defined in a level-wise bottom-up approach dealing with the combinatorial explosion problem. As shown in experiments on real financial time series, our work can be further used to efficiently enhance the temporal rule mining process on time series.

Keywords

Time Series Frequent Pattern Hash Table Pattern Mining Support Count 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Allen, J.F.: Maintaining Knowledge about Temporal Intervals. Communications of the ACM 26, 832–843 (1983)CrossRefzbMATHGoogle Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: VLDB, pp. 487–499 (1994)Google Scholar
  3. 3.
    Batal, I., Fradkin, D., Harrison, J., Mörchen, F., Hauskrecht, M.: Mining Recent Temporal Patterns for Event Detection in Multivariate Time Series Data. In: KDD, pp. 280–288 (2012)Google Scholar
  4. 4.
    Batyrshin, I., Sheremetov, L., Herrera-Avelar, R.: Perception Based Patterns in Time Series Data Mining. In: Batyrshin, I., Kacprzyk, J., Sheremetov, L., Zadeh, L.A. (eds.) Perception-based Data Mining and Decision Making in Economics and Finance. SCI, vol. 36, pp. 85–118. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Dorr, D.H., Denton, A.M.: Establishing Relationships among Patterns in Stock Market Data. Data & Knowledge Engineering 68, 318–337 (2009)CrossRefGoogle Scholar
  6. 6.
    Financial Time Series, http://finance.yahoo.com/ (accessed by May 23, 2013)
  7. 7.
    Fu, T.: A Review on Time Series Data Mining. Engineering Applications of Artificial Intelligence 24, 164–181 (2011)CrossRefGoogle Scholar
  8. 8.
    Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation. In: Proc. the 2000 ACM SIGMOD, pp. 1–12 (2000)Google Scholar
  9. 9.
    Kacprzyk, J., Wilbik, A., Zadrożny, S.: On Linguistic Summarization of Numerical Time Series Using Fuzzy Logic with Linguistic Quantifiers. In: Chountas, P., Petrounias, I., Kacprzyk, J. (eds.) Intelligent Techniques and Tools for Novel System Architectures. SCI, vol. 109, pp. 169–184. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Mörchen, F., Ultsch, A.: Efficient Mining of Understandable Patterns from Multivariate Interval Time Series. Data Min. Knowl. Disc. 15, 181–215 (2007)CrossRefGoogle Scholar
  11. 11.
    Mueen, A., Keogh, E., Zhu, Q., Cash, S.S., Westover, M.B., BigdelyShamlo, N.: A Disk-Aware Algorithm for Time Series Motif Discovery. Data Min. Knowl. Disc. 22, 73–105 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Sacchi, L., Larizza, C., Combi, C., Bellazzi, R.: Data Mining with Temporal Abstractions: Learning Rules from Time Series. Data Min. Knowl. Disc. 15, 217–247 (2007)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Struzik, Z.R.: Time Series Rule Discovery: Tough, not Meaningless. In: Proc. the Int. Symposium on Methodologies for Intelligent Systems, pp. 32–39 (2003)Google Scholar
  14. 14.
    Tanaka, Y., Iwamoto, K., Uehara, K.: Discovery of Time Series Motif from Multi-dimensional Data Based on MDL Principle. Machine Learning 58, 269–300 (2005)CrossRefzbMATHGoogle Scholar
  15. 15.
    Tang, H., Liao, S.S.: Discovering Original Motifs with Different Lengths from Time Series. Knowledge-Based Systems 21, 666–671 (2008)CrossRefGoogle Scholar
  16. 16.
    Yang, Q., Wu, X.: 10 Challenging Problems in Data Mining Research. International Journal of Information Technology & Decision Making 5, 597–604 (2006)CrossRefGoogle Scholar
  17. 17.
    Yoon, J.P., Luo, Y., Nam, J.: A Bitmap Approach to Trend Clustering for Prediction in Time Series Databases. In: Data Mining and Knowledge Discovery: Theory, Tools, and Technology II (2001)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Faculty of Computer Science & EngineeringHCMC Uni. of TechnologyHanoiVietnam

Personalised recommendations