Abstract
This study proposes a temporal data mining method to discover qualitative and quantitative patterns in time series databases. The method performs discrete-valued time series (DIS) analysis on time series databases to search for any similarity and periodicity of patterns that are used for knowledge discovery. In our method there are three levels for mining patterns. At the first level, a structural search based on distance measure models is employed to find pattern structures; the second level performs a value-based search on the discovered patterns using a local polynomial analysis; the third level, based on hidden Markov models (HMMs), finds global patterns from a DTS set. As a result, similar and periodic patterns are successfully extracted. We demonstrate our method on the analysis of “Exchange Rate Patterns” between the U.S. dollar and Australian dollar.
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References
Agrawal, It., Srikant, R. (19.94): Fast algorithms for mining association rules in large databases. In: Bocca, J., Jarke, M., Zaniolo, C. (Eds.), 20th International Conference on Very Large Data Bases, September 12–15, 1994, Santiago, Chile Proceedings, 487–499, Morgan Kaufmann Publishers
Agrawal, R., Lin, K. -I.., Sawhney, H. S., Shim, K. (1995): [fast similarity search in the presence of noise, scaling, and translation in time-series databases. In: Proceedings of the 21st VLDB Conference, 990 501, Morgan Kaufmann
Agrawal, R., Psaila, G., Wimmers, E. L., Zait, M. (1995): Querying shapes of histories. 1n: Proceedings of the 21st VLDB Conference, 990501, Morgan Kaufmann
Baxter, R. A., Williams, G. J., He, H. (2001): Feature selection for temporal health records. available from Craham.Williamsacbr.dit.csiro.au, APR.
Das, G., Lin, K., Mannila, H., Renganathan, G., Smyth, P. (1998): Rule discovery from time series. In: Proceedings of the international conference on KDD and Data Mining (KDD-98)
Han, J. W., Yin, Y., Dong, G. (1999): Efficient mining of partial periodic patterns in time series database. In: Proceedings of the 15th international conference on Data Engineering, Sydney, Australia, 106–115, IEEE CS Press, March
Elder IV, J., Pregibon, D. (1995): A statistical perspective on knowledge discovery in databases. In: Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., 1 tlnirusarny, R. (Eds.), Advances in Knowledge Discovery and Data Mining, 83 115. The MIT Press
MacQueen, J. B. (1967): Some methods for classification and analysis of multivariate observations. In: 5th Berkeley Symposium on Mathematical Statistics and Probability, 281–297
Lee,.1. Y., Elmasri, R., Won,.1. (1997): An integrated temporal data model incorporating time series concept
Li, C., Biswas, G. (1999): Temporal pattern generation using hidden rnarkov model based unsuperised classification. In: Proceedings of the 3rd Cnternational Symposium, Springer-Verlag, LNCS 1692, 245–256
Lin, W. Q., Orgun, M. A. (1999): Applied hidden periodicity analysis for raining discrete-valued time series. In: Proceedings of IS LIP-99, 56–68, Demokritos Institute, Athens, Greece
Lin, W. Q., Orgun, M. A. (2000): Mining patterns in discrete time series databases. In: Intensional programming 1I, World Scientific, 182–196
Lin, W. Q., Orgun, M. A., Williams, G. (2000): Temporal data mining using multilevel-local polynomial models. In: Proceedings of IDEAL-2000, The Chinese University of Hong Kong, Hong Kong, Springer-Verlag, 180–186, LNCS 1983, December
Lin, W. Q., Orgun, M. A.,Williatns, G. (2001): Multilevels hidden rnarkov models for temporal (tata mining. En: Temporal data mining workshop Proceedings of KDD-2001, San Franciso, California, USA
Lin, W. Q., Orgun, M. A.,Williarns, G. (2001): Temporal data mining using local polynomial-hiddenmarlcov models. In: Proceedings of PA KDD-200 I. The University of Hong Kong, Hong Kong, Springer-Verlag, 324 335, LNCS 2015, April
\lacDonald, I. L., Zucchini, W. (1997): Hidden Markov and oilier models for discrete-valued time series. Chapman and Hall
Keilson,.1. (1979): Marko Chain models-Rarity and Exponentiality. In: Applied Mathematical Sciences Volurvn 28, Springer-Verlag, 15 19
Zhongjie, X. (1993): Case Studies in’ Time Series Analysis. World Scientific 12.19 Hatfield, C. ( 1992 ): The Analysis of Time Series. Chapman I lall
Fan, J., Gijbels, 1. (1992): Local Polynomial Modelling and Its Applications. Chapman and Hall
Mannila, H., Bollobas, B. (1997): Time-series similarity problem and well-separated geometric sets. In: Proc. 13th Annual ACM symposium on computational geometry, 454–476, Nice, France
Ng, M. K., Huang, Z. (1997): Temporal data mining with a case study of astronomical data analysis. In G. Golub, S. H. Lui, F. Luk, and R. Plemmons, editors, Proceedings of the Workshop on Scientific Computing 97, 258–264. Springer-Verlag, Hong Kong, March
Jajodia, S., Sripada, S., Etzion, O. (1998): Temporal Databases: Research and Practice. 281–309, LNCS1399, Springer-Verlag
Wong, A. K. C., Wang, Y. (1997): High-order pattern discovery from discrete-valued data. IEEE Transaction on Knowledge and Data Engineering, 9(6): 887–892, Nov., Dec
Huang, Z. (1997): Clustering large data set with mixed numeric and categorical values. In: ist Pacific-Asia Conference on Knowledge Discovery and Data Mining, Singapore, World Scientific, 21–34, February
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Lin, W., Orgun, M.A., Williams, G.J. (2004). Structural Pattern Discovery in Time Series Databases. In: Chen, SH., Wang, P.P. (eds) Computational Intelligence in Economics and Finance. Advanced Information Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-06373-6_12
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DOI: https://doi.org/10.1007/978-3-662-06373-6_12
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