Abstract
Time-series prediction has been very well researched by both the Statistical and Data Mining communities. However the multiple time-series problem of predicting simultaneous movement of a collection of time sensitive variables which are related to each other has received much less attention. Strong relationships between variables suggests that trajectories of given variables that are involved in the relationships can be improved by including the nature and strength of these relationships into a prediction model. The key challenge is to capture the dynamics of the relationships to reflect changes that take place continuously over time. In this research we propose a novel algorithm for extracting profiles of relationships through an evolving clustering method. We use a form of non-parametric regression analysis to generate predictions based on the profiles extracted and historical information from the past. Experimental results on a real-world climatic data reveal that the proposed algorithm outperforms well established methods of time-series prediction.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Collins, D., Biekpe, N.: Contagion and Interdependence in African Stock Markets. The South African Journal of Economics 71(1), 181–194 (2003)
Masih, A., Masih, R.: Dynamic Modeling of Stock Market Interdependencies: An Empirical Investigation of Australia and the Asian NICs. Working Papers 98-18, pp. 1323–9244. University of Western Australia (1998)
Antoniou, A., Pescetto, G., Violaris, A.: Modelling International Price Relationships and Interdependencies between the Stock Index and Stock Index Future Markets of Three EU Countries: A Multivariate Analysis. Journal of Business, Finance and Accounting 30, 645–667 (2003)
Kasabov, N., Chan, Z., Jain, V., Sidorov, I., Dimitrov, D.: Gene Regulatory Network Discovery from Time-series Gene Expression Data: A Computational Intelligence Approach. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds.) ICONIP 2004. LNCS, vol. 3316, pp. 1344–1353. Springer, Heidelberg (2004)
Friedman, L., Nachman, P.: Using Bayesian Networks to Analyze Expression Data. Journal of Computational Biology 7, 601–620 (2000)
Liu, B., Liu, J.: Multivariate Time Series Prediction via Temporal Classification. In: Proc. IEEE ICDE 2002, pp. 268–275. IEEE, Los Alamitos (2002)
Kim, T., Adali, T.: Approximation by Fully Complex Multilayer Perceptrons. Neural Computation 15, 1641–1666 (2003)
Yang, H., Chan, L., King, I.: Support Vector Machine Regression for Volatile Stock Market Prediction. In: Yellin, D.M. (ed.) Attribute Grammar Inversion and Source-to-source Translation. LNCS, vol. 302, pp. 143–152. Springer, Heidelberg (1988)
Zanghui, Z., Yau, H., Fu, A.M.N.: A new stock price prediction method based on pattern classification. In: Proc. IJCNN 1999, pp. 3866–3870. IEEE, Los Alamitos (1999)
Holland, J.H., Holyoak, K.J., Nisbett, R.E., Thagard, P.R.: Induction: Processes of Inference, Learning and Discovery, Cambridge, MA, USA (1989)
Kasabov, N.: Global, Local and Personalised Modelling and Pattern Discovery in Bioinformatics: An Integrated Approach. Pattern Recognition Letters 28, 673–685 (2007)
Song, Q., Kasabov, N.: ECM - A Novel On-line Evolving Clustering Method and Its Applications. In: Posner, M.I. (ed.) Foundations of Cognitive Science, pp. 631–682. MIT Press, Cambridge (2001)
Rodrigues, P., Gama, J., Pedroso, P.: Hierarchical Clustering of Time-Series Data Streams. IEEE TKDE 20(5), 615–627 (2008)
Ben-Dor, A., Shamir, R., Yakhini, Z.: Clustering Gene Expression Patterns. Journal of Computational Biology 6(3/4), 281–297 (1999)
Widiputra, H., Kho, H., Lukas, Pears, R., Kasabov, N.: A Novel Evolving Clustering Algorithm with Polynomial Regression for Chaotic Time-Series Prediction. In: Leung, C.S., Lee, M., Chan, J.H. (eds.) ICONIP 2009. LNCS, vol. 5864, pp. 114–121. Springer, Heidelberg (2009)
Vitousek, P.M.: Beyond Global Warming: Ecology and Global Change. Ecology 75(7), 1861–1876 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Widiputra, H., Pears, R., Kasabov, N. (2011). Multiple Time-Series Prediction through Multiple Time-Series Relationships Profiling and Clustered Recurring Trends. In: Huang, J.Z., Cao, L., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2011. Lecture Notes in Computer Science(), vol 6635. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20847-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-20847-8_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20846-1
Online ISBN: 978-3-642-20847-8
eBook Packages: Computer ScienceComputer Science (R0)