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 temporal patterns. At the first level, a structural method based on distance measures through polynomial modelling is employed to find pattern structures; the second level performs a value-based search using local polynomial analysis; and then the third level based on multilevel-local polynomial models(MLPMs), finds global patterns from a DTS set. We demonstrate our method on the analysis of “Exchange Rates Patterns” between the U.S. dollar and Australian dollar.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
T. W. Anderson. An introduction to Multivariate Statistical Analysis. Wiley, New York, 1984.
C. Bettini. Mining temportal relationships with multiple granularities in time sequences. IEEE Transactions on Data & Knowledge Engineering, 1998.
J. W. Han, Y. Yin, and G. Dong. Efficient mining of partial periodic patterns in time series database. Ieee Trans. On Knowledge And Data Engineering, 1998.
J. Fan and I. Gijbels, editors. Local polynomial Modelling and Its Applications. Chapman and hall, 1996.
Michael K. Ng and Zhexue Huang. 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, pages 258–264. Springer-Verlag, Hong Kong, March 1997.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lin, W., Orgun, M.A., Williams, G.J. (2000). Temporal Data Mining Using Multilevel-Local Polynomial Models. In: Leung, K.S., Chan, LW., Meng, H. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents. IDEAL 2000. Lecture Notes in Computer Science, vol 1983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44491-2_27
Download citation
DOI: https://doi.org/10.1007/3-540-44491-2_27
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41450-6
Online ISBN: 978-3-540-44491-6
eBook Packages: Springer Book Archive