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Configuration of neural networks for the analysis of seasonal time series

Taskaya-Temizel, T, Casey, MC, Singh, S, Singh, M, Apte, C and Perner, P (2005) Configuration of neural networks for the analysis of seasonal time series In: 3rd International Conference on Advances in Pattern Recognition, 2005-08-22 - 2005-08-25, Bath, ENGLAND.

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Time series often exhibit periodical patterns that can be analysed by conventional statistical techniques. These techniques rely upon an appropriate choice of model parameters that are often difficult to determine. Whilst neural networks also require an appropriate parameter configuration, they offer a way in which non-linear patterns may be modelled. However, evidence from a limited number of experiments has been used to argue that periodical patterns cannot be modelled using such networks. In this paper, we present a method to overcome the perceived limitations of this approach by determining the configuration parameters of a time delayed neural network from the seasonal data it is being used to model. Our method uses a fast Fourier transform to calculate the number of input tapped delays, with results demonstrating improved performance as compared to that of other linear and hybrid seasonal modelling techniques.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords: Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, Theory & Methods, Computer Science, ARIMA, MODEL
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Divisions: Faculty of Engineering and Physical Sciences > Computing Science
Depositing User: Symplectic Elements
Date Deposited: 17 Jun 2011 15:56
Last Modified: 23 Sep 2013 18:42

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