Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/8003
Title: Combining local and global models to capture fast and slow dynamics in time series data
Authors: Small, M
Issue Date: 2004
Publisher: Springer
Source: In ZR Yang, H Yin & RM Everson (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2004 : 5th International Conference, Exeter, UK. August 25-27, 2004. Proceedings, p. 648-653. Berlin : Springer, 2004 How to cite?
Series/Report no.: Lecture Notes in Computer Science ; v. 3177
Abstract: Many time series exhibit dynamics over vastly different time scales. The standard way to capture this behavior is to assume that the slow dynamics are a "trend", to de-trend the data, and then to model the fast dynamics. However, for nonlinear dynamical systems this is generally insufficient. In this paper we describe a new method, utilizing two distinct nonlinear modeling architectures to capture both fast and slow dynamics. Slow dynamics are modeled with the method of analogues, and fast dynamics with a deterministic radial basis function network. When combined the resulting model out-performs either individual system.
URI: http://hdl.handle.net/10397/8003
ISSN: 0302-9743
DOI: 10.1007/978-3-540-28651-6_95
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