Time series forecasting methodology for multiple-step-ahead prediction

Pavlidis, N. G. and Tasoulis, D. K. and Vrahatis, M. N. (2005) Time series forecasting methodology for multiple-step-ahead prediction. In: Proceedings of the IASTED International Conference on Computational Intelligence. Proceedings of the IASTED International Conference on Computational Intelligence . UNSPECIFIED, CAN, pp. 456-461. ISBN 0889864810

Full text not available from this repository.

Abstract

This paper presents a time series forecasting methodology and applies it to generate multiple-step-ahead predictions for the direction of change of the daily exchange rate of the Japanese Yen against the US Dollar. The proposed methodology draws from the disciplines of chaotic time series analysis, clustering, and artificial neural networks. In brief, clustering is applied to identify neighborhoods in the reconstructed state space of the system; and subsequently neural networks are trained to model the dynamics of each neighborhood separately. The results obtained through this approach are promising.

Item Type: Contribution in Book/Report/Proceedings
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/2200
Subjects:
Departments: Faculty of Science and Technology > Mathematics and Statistics
ID Code: 138570
Deposited By: ep_importer_pure
Deposited On: 05 Nov 2019 10:50
Refereed?: Yes
Published?: Published
Last Modified: 01 Jan 2020 11:01
URI: https://eprints.lancs.ac.uk/id/eprint/138570

Actions (login required)

View Item View Item