Multiple Time-Series Prediction through Multiple Time-Series Relationships Profiling and Clustered Recurring Trends
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.
Keywordstime-series inter-relationships multiple time-series prediction evolving clustering method recurring trends
Unable to display preview. Download preview PDF.
- 2.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)Google Scholar
- 4.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)CrossRefGoogle Scholar
- 6.Liu, B., Liu, J.: Multivariate Time Series Prediction via Temporal Classification. In: Proc. IEEE ICDE 2002, pp. 268–275. IEEE, Los Alamitos (2002)Google Scholar
- 8.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)Google Scholar
- 9.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)Google Scholar
- 10.Holland, J.H., Holyoak, K.J., Nisbett, R.E., Thagard, P.R.: Induction: Processes of Inference, Learning and Discovery, Cambridge, MA, USA (1989)Google Scholar
- 12.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)Google Scholar
- 13.Rodrigues, P., Gama, J., Pedroso, P.: Hierarchical Clustering of Time-Series Data Streams. IEEE TKDE 20(5), 615–627 (2008)Google Scholar