Abstract
This study proposes a data mining framework to discover qualitative and quantitative patterns in discrete-valued time series (DTS). In our method, there are three levels for mining similarity and periodicity patterns. At the first level, a structural-based 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 local polynomial analysis; and then the third level based on hidden Markov-local polynomial models (HMLPMs), finds global patterns from a DTS set.We demonstrate our method on the analysis of“Exchange Rates Patterns” between the U.S. dollar and the United Kingdom Pound.
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References
Rakesh Agrawal, King-Ip Lin, Harpreet S. Sawhney, and Kyuseok Shim. Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In Proceedings of the 21st VLDB Conference, Zürich, Switzerland, 1995.
Rakesh Agrawal, Giuseppe Psaila, Edward L. Wimmers, and Mohamed Zait. Querying shapes of histories. In Proceedings of the 21st VLDB Conference, 1995.
T.W. Anderson. An introduction to Multivariate Statistical Analysis. Wiley, NewYork, 1984.
C. Bettini. Mining temportal relationships with multiple granularities in time sequences. IEEE Transactions on Data & Knowledge Engineering, 1998.
H. Mannila G. Renganathan G. Das, K. Lin and P. Smyth. Rule discovery from time series. In Proceedings of the international conference on KDD and Data Mining(KDD-98), 1998.
D. Gunopulos G. Das and H. Mannila. Finding similar time seies. In Principles of Knowledge Discovery and Data Mining’ 97, 1997.
P. Guttort. Stochastic Modeling of Scientific Data. Chapman & Hall, London, 1995.
J. Fan and I. Gijbels, editors. Local polynomial Modelling and Its Applications. Chapman and hall, 1996.
Cen Li and Gautam Biswas. Temporal pattern generation using hidden markov model based unsuperised classification. In Proc. of IDA-99, pages 245–256, 1999.
Wei Q. Lin and Mehmet A. Orgun. Temporal data mining using hidden periodicity analysis. In Proceedings of ISMIS 2000, University of North Carolina, USA, 2000.
Wei Q. Lin, MehmetA. Orgun, and Graham Williams. Temporal data mining using multilevellocal polynomial models. In Proceedings of IDEAL 2000, The Chinese University of Hongkong, Hong Kong, 2000.
S. Jajodia and S. Sripada O. Etzion, editor. Temporal databases: Research and Practice. Springer-Verlag,LNCS1399, 1998.
P. Baldi and S. Brunak. Bioinformatics & The Machine Learning Approach. The MIT Press, 1999.
Z. Huang. Clustering large data set with mixed numeric and categorical values. In 1st Pacific-Asia Conference on Knowledge Discovery and Data Mining, 1997
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Lin, W., Orgun, M.A., Williams, G.J. (2001). Temporal Data Mining Using Hidden Markov-Local Polynomial Models. In: Cheung, D., Williams, G.J., Li, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2001. Lecture Notes in Computer Science(), vol 2035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45357-1_35
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DOI: https://doi.org/10.1007/3-540-45357-1_35
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