Abstract
The cross-sectional time series data means a group of multivariate time series each of which has the same set of variables. Usually its length is short. It occurs frequently in business, economics, science, and so on. We want to mine rules from it, such as ”GDP rises if Investment rises in most provinces” in economic analysis. Rule mining is divided into two steps: events distilling and association rules mining. This paper concentrates on the former and applies Apriori to the latter. The paper defines event types based on relative differences. Considering cross-sectional property, we introduce an ANOVA-based event-distilling method which can gain proper events from cross-sectional time series. At last, the experiments on synthetic and real-life data show the advantage of ANOVA-based event-distilling method and the influential factors, relatively to the separately event-distilling method.
This work is supported by both the National High Technology Research and Development Program of China (863 Program) under Grant No.2002AA444120 and the National Key Basic Research and Development Program of China (973 Program) under Grant No.2002CB312006.
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References
Agrawal, R., Psaila, G., Wimmers, E.L., Zaot, M.: Querying shapes of histories. In: Dayal, U., Gray, P.M.D., Nishio, S. (eds.) Proc. of VLDB 1995, Zurich, Switzerland, pp. 502–514. Morgan Kaufmann Publishers, Inc., San Francisco (1995)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proc. of VLDB 1994, pp. 12–15. Morgan Kaufmann, San Francisco (1994)
Das, G., Lin, K.-I., Mannila, H., Renganathan, G., Smyth, P.: Rule discovery from time series. Knowledge Discovery and Data Mining, 16–22 (1998)
Freund, J.E.: Mathematical Statistics, 6th edn. Prentice-Hall, Englewood Cliffs (1999)
Judge, G.G., Hill, R.C., Griffiths, W.E., Lütkepohl, H., Lee, T.-C.: Introduction to the Theory and Practice of Econometrics, 2nd edn. John Wiley and Sons Ltd., Chichester (1988)
Keogh, E., Lonardi, S., Chiu, B.: Finding surprising patterns in a time series database in linear time and space. In: Proc. of KDD 2002, Edmonton, Alberta, Canada (July 2002)
Mannila, H., Toivonen, H., Inkeri Verkamo, A.: Efficient algorithms for discovering association rules. In: Fayyad, U.M., Uthurusamy, R. (eds.) AAAI Workshop on Knowledge Discovery in Databases (KDD 1994), Seattle, Washington, pp. 181–192. AAAI Press, Menlo Park (1994)
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Luo, K., Wang, J., Sun, J. (2004). Rules Discovery from Cross-Sectional Short-Length Time Series. In: Dai, H., Srikant, R., Zhang, C. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2004. Lecture Notes in Computer Science(), vol 3056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24775-3_72
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DOI: https://doi.org/10.1007/978-3-540-24775-3_72
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