RULE EVOLUTION APPROACH FOR MINING MULTIVARIATE TIME SERIES DATA

Viet-An Nguyen, Vivekanand Gopalkrishnan

2008

Abstract

In the last few decades, due to the massive explosion of data over the world, time series data mining has attracted numerous researches and applications. Most of these works only focus on time series of a single attribute or univariate time series data. However, in many real world problems, data arrive as one or many series of multiple attributes, i.e., they are multivariate time series data. Because they contain multiple attributes, multivariate time series data promise to provide more intrinsic correlations among them, from which more important information can be gathered and extracted. In this paper, we present a novel approach to model and predict the behaviors of multivariate time series data based on a rule evolution methodology. Our approach is divided into distinct steps, each of which can be accomplished by several machine learning techniques. This makes our system highly flexible, configurable and extendable. Experiments are also conducted on real S&P 500 stock data to examine the effectiveness and reliability of our approach. Empirical results demonstrate that our system has substantial estimation reliability and prediction accuracy.

References

  1. Breiman, L., Friedman, J., Olshen, R., and Stone, C. (1984). Classification and regression trees. Chapman and Hall, New York, 368 p.
  2. Cendrowska, J. (1987). Prism: An algorithm for inducing modular rules. International Journal of Man-Machine Studies, 27(4):349-370.
  3. Geng, L. and Hamilton, H. J. (2006). Interestingness measures for data mining: A survey. ACM Computing Survey, 38(3):9.
  4. Hetland, M. and Saetrom, P. (2002). Temporal rule discovery using genetic programming and specialized hardware. In 4th International Conference on Recent Advances in Soft Computing, RASC.
  5. Holte, R. (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning, 11:63-91.
  6. Kadous, M. W. (1999). Learning comprehensible descriptions of multivariate time series. In ICML, pages 454- 463.
  7. Keogh, E. and Kasetty, S. (2002). On the need for time series data mining benchmarks: a survey and empirical demonstration. In KDD 7802: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 102- 111, New York, NY, USA. ACM.
  8. Kohavi, R. (1995). The power of decision tables. In 8th European Conference on Machine Learning, pages 174- 189. Springer.
  9. Last, M., Kandel, A., and Bunke, H. (2004). Data Mining in Time series databases. Series in Machine Perception and Artificial Intelligence. World Scientific.
  10. Last, M., Klein, Y., and Kandel, A. (2001). Knowledge discovery in time series databases. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 31(1):160- 169.
  11. Povinelli, P. (2000). Using genetic algorithms to find temporal patterns indicative of time series events. In GECCO 2000 Workshop: Data Mining with Evolutionary Algorithsm, pages 80-84.
  12. Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1):81-106.
  13. Quinlan, J. R. (1993). C4.5: Programs for machine learning. Morgan Kaufmann Publishers Inc.
  14. Su, J. and Zhang, H. (2006). A fast decision tree learning algorithm. In Proceedings of The Twenty-First National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference (AAAI/IAAI), July 16-20, 2006, Boston, Massachusetts, USA.
  15. Tan, P.-N., Kumar, V., and Srivastava, J. (2002). Selecting the right interestingness measure for association patterns. In KDD 7802: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 32-41, New York, NY, USA. ACM.
  16. Tukey, J. W. (1977). Exploratory Data Analysis. AddisonWesley, Reading, MA.
  17. Wah, B. W. and Qian, M. (2002). Constrained formulations and algorithms for stock-price predictions using recurrent FIR neural networks. In Proceedings of The Twenty-First National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference (AAAI/IAAI), pages 211-216, Edmonton, Alberta, Canada.
  18. Wei, L., Kumar, N., Lolla, V. N., Keogh, E. J., Lonardi, S., Ratanamahatana, C. A., and Herle, H. V. (2005). A practical tool for visualizing and data mining medical time series. In CBMS, pages 341-346.
  19. Weiss, G. and Hirsh, H. (1998). Learning to predict rare events in event sequences. In 4th Conference on Knowledge Discovery and Data Mining KDD, pages 359-363, Menlo Park, CA, USA. AAAI Press.
  20. Witten, I. H. and Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann, second edition.
Download


Paper Citation


in Harvard Style

Nguyen V. and Gopalkrishnan V. (2008). RULE EVOLUTION APPROACH FOR MINING MULTIVARIATE TIME SERIES DATA . In Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8111-37-1, pages 19-26. DOI: 10.5220/0001688500190026


in Bibtex Style

@conference{iceis08,
author={Viet-An Nguyen and Vivekanand Gopalkrishnan},
title={RULE EVOLUTION APPROACH FOR MINING MULTIVARIATE TIME SERIES DATA},
booktitle={Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2008},
pages={19-26},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001688500190026},
isbn={978-989-8111-37-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - RULE EVOLUTION APPROACH FOR MINING MULTIVARIATE TIME SERIES DATA
SN - 978-989-8111-37-1
AU - Nguyen V.
AU - Gopalkrishnan V.
PY - 2008
SP - 19
EP - 26
DO - 10.5220/0001688500190026