AN EFFICIENT FRAMEWORK FOR ITERATIVE TIME-SERIES TREND MINING

Ajumobi Udechukwu, Ken Barker, Reda Alhajj

Abstract

Trend analysis has applications in several domains including: stock market predictions, environmental trend analysis, sales analysis, etc. Temporal trend analysis is possible when the source data (either business or scientific) is collected with time stamps, or with time-related ordering. These time stamps (or orderings) are the core data points for time sequences, as they constitute time series or temporal data. Trends in these time series, when properly analyzed, lead to an understanding of the general behavior of the series so it is possible to more thoroughly understand dynamic behaviors found in data. This analysis provides a foundation for discovering pattern associations within the time series through mining. Furthermore, this foundation is necessary for the more insightful analysis that can only be achieved by comparing different time series found in the source data. Previous works on mining temporal trends attempt to efficiently discover patterns by optimizing discovery processes in a single run over the data. The algorithms generally rely on user-specified time frames (or time windows) that guide the trend searches. Recent experience with data mining clearly indicates that the process is inherently iterative, with no guarantees that the best results are achieved in the first run. If the existing approaches are used for iterative analysis, the same heavy weight process would be re-run on the data (with varying time windows) in the hope that new discoveries will be made on subsequent iterations. Unfortunately, this heavy weight re-execution and processing of the data is expensive. In this work we present a framework in which all the frequent trends in the time series are computed in a single run (in linear time), thus eliminating expensive re-computations in subsequent iterations. We also demonstrate that trend associations within the time series or with related time series can be found.

References

  1. Agrawal, R., Psaila, G., Wimmers, E.L., and Zait, M., 1995. Querying Shapes of Histories, Proceedings of the 21st VLDB Conference, Zurich, Switzerland.
  2. Allen, J.F., 1983. Maintaining Knowledge about Temporal Intervals, Comm. ACM, 26(11):832-843.
  3. Bieganski, P., Riedl, J., Carlis, J.V., and Retzel, E.R., 1994. Generalized Suffix Trees for Biological Sequence Data: Applications and Implementation, In Proceedings of the 27th Hawaii Int'l Conference on Systems Science, IEEE Computer Society Press, pages 35-44.
  4. Das, G., Lin, K-I., Mannila, H., Ranganathan, G., and Smyth, P., 1998. Rule Discovery from Time Series, Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, [KDD98], New York, NY, pages 16-22.
  5. Faloutsos, C., Ranganathan, M., and Manolopoulos, Y., 1994. Fast Subsequence Matching in Time-Series Databases, in Proceedings of the 1994 ACM SIGMOD International Conference on Management of Data, pages 419-429.
  6. Gusfield, D., 1997. Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology, Cambridge University Press.
  7. Han, J., Gong, W., and Yin, Y., 1998. Mining SegmentWise Periodic Patterns in Time Series Databases, KDD, pages 214-218.
  8. Harms, S.K., Deogun, J., Saquer, J., and Tadesse, T., 2001. Discovering Representative Episodal Association Rules from Event Sequences Using Frequent Closed Episode Sets and Event Constraints, Proceedings of the IEEE International Conference on Data Mining, Silicon Valley, CA, pages 603-606.
  9. Hoppner, F., 2001. Discovery of Temporal Patterns, Learning Rules about the Qualitative Behaviour of Time Series, in De Raedt, L., Siebes, A., (Eds.), PKDD 2001, LNAI 2168, Springer-Verlag, Berlin, pages 192-203.
  10. Indyk, P., Koudas, N., and Muthukrishnan, S., 2000. Identifying Representative Trends in Massive Time Series Data Sets Using Sketches, In Proceedings of the 26th Int'l Conference on Very Large Data Bases, Cairo, Egypt, pages 363-372.
  11. Keogh, E., 2003. The UCR Time Series Data Mining Archive, http://www.cs.ucr.edu/eamonn/TSDMA/index.html, University of California - Computer Science and Engineering Department, Riverside, CA.
  12. Keogh, E.J., Chakrabarti, K., Pazzani, M.J., and Mehrotra, S., 2000. Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases, Journal of Knowledge and Information Systems, vol 3, number 3, pages 263-286.
  13. Mannila, H., Toivonen, H., and Verkamo, A.I., 1997. Discovery of Frequent Episodes in Event Sequences, Report C-1997-15, Department of Computer Science, University of Helsinki, Finland.
  14. Patel, P., Keogh, E., Lin, J., and Lonardi, S., 2002. Mining Motifs in Massive Time Series Databases, Proceedings of the IEEE Int'l Conference on Data Mining, Maebashi City, Japan.
  15. Perng, C-S., Wang, H., Zhang, S.R., and Parker, D.S., 2000. Landmarks: A New Model for Similarity-, Based Pattern Querying in Time Series Databases, Proceedings of the 16th IEEE International Conference on Data Engineering.
  16. Qu, Y., Wang, C., Wang, X.S., 1998. Supporting Fast Search in Time Series for Movement Patterns in Multiple Scales, Proceedings of the ACM 7th International Conference on Information Management, pages 251-258.
  17. West, M., 2003. Some Time Series Data Sets, retrieved June 18, 2003, from http://www.stat.duke.edu/mw/ts_data_sets.html, Duke University.
  18. Yi, B-K, Faloutsos, C., 2000. Fast Time Sequence Indexing for Arbitrary Lp norms, in The VLDB Journal, pages 385-394.
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Paper Citation


in Harvard Style

Udechukwu A., Barker K. and Alhajj R. (2004). AN EFFICIENT FRAMEWORK FOR ITERATIVE TIME-SERIES TREND MINING . In Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 972-8865-00-7, pages 130-137. DOI: 10.5220/0002620001300137


in Bibtex Style

@conference{iceis04,
author={Ajumobi Udechukwu and Ken Barker and Reda Alhajj},
title={AN EFFICIENT FRAMEWORK FOR ITERATIVE TIME-SERIES TREND MINING},
booktitle={Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2004},
pages={130-137},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002620001300137},
isbn={972-8865-00-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - AN EFFICIENT FRAMEWORK FOR ITERATIVE TIME-SERIES TREND MINING
SN - 972-8865-00-7
AU - Udechukwu A.
AU - Barker K.
AU - Alhajj R.
PY - 2004
SP - 130
EP - 137
DO - 10.5220/0002620001300137