Configuration of Neural Networks for the Analysis of Seasonal Time Series

Autor:	Taskaya-Temizel, T, Casey, MC, Singh, S, Singh, M, Apte, C, Perner, P
Rok vydání:	2005
Předmět:	Series (mathematics) Artificial neural network Mean squared error Computer science business.industry Fast Fourier transform Pattern recognition (psychology) Artificial intelligence Time series business Algorithm
Zdroj:	Pattern Recognition and Data Mining ISBN: 9783540287575 ICAPR (1)
DOI:	10.1007/11551188_32
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::59e0aacbe09d504e08880ea290bfe058 https://doi.org/10.1007/11551188_32 Zobrazit plný text záznamu