An empirical analysis of a neural network model for the time series forecasting of different industrial segments

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dc.contributor.authorZOUCAS, F. A. M.
dc.contributor.authorBELFIORE, P.
dc.date.accessioned2022-01-12T22:00:11Z
dc.date.available2022-01-12T22:00:11Z
dc.date.issued2015
dc.description.abstractCopyright © 2015 Inderscience Enterprises Ltd.This paper aims to propose a neural network model for forecasting the production time series of 11 different industries in Brazil. The data was collected from Brazilian Institute of Geography and Statistics (IBGE). Firstly, we study different networks topologies that have been implemented in the literature in recent years, such as perceptron, linear networks, multi-layer perceptron (MLP), probabilistic network, Hopfield model, Kohonen model, time delay neural network (TDNN), Elman and Jordan network, in addition to the backpropagation and Levenberg-Marquadt algorithms. Studying the behaviour of these time series and the main characteristics of the each network topology, we conclude that the TDNN with multi-layer perceptron is the best to estimate the production time series of 11 industrial segments. The neural network was then applied considering two different strategies of structural model. We conclude that the neural network model proposed was effective for forecasting production time series in these industries.
dc.description.firstpage261
dc.description.issuenumber3
dc.description.lastpage283
dc.description.volume8
dc.identifier.citationZOUCAS, F. A. M.; BELFIORE, P. An empirical analysis of a neural network model for the time series forecasting of different industrial segments. International Journal of Applied Decision Sciences, v. 8, n. 3, p. 261-283, 2015.
dc.identifier.doi10.1504/IJADS.2015.072145
dc.identifier.issn1755-8085
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/3983
dc.relation.ispartofInternational Journal of Applied Decision Sciences
dc.rightsAcesso Restrito
dc.subject.otherlanguageIndustrial segments
dc.subject.otherlanguageMLP
dc.subject.otherlanguageMulti-layer perceptron
dc.subject.otherlanguageNeural networks
dc.subject.otherlanguageTime delay
dc.titleAn empirical analysis of a neural network model for the time series forecasting of different industrial segments
dc.typeArtigo
fei.scopus.citations7
fei.scopus.eid2-s2.0-84943410330
fei.scopus.subjectEmpirical analysis
fei.scopus.subjectIndustrial segments
fei.scopus.subjectMulti layer perceptron
fei.scopus.subjectNeural network model
fei.scopus.subjectProbabilistic network
fei.scopus.subjectStructural modeling
fei.scopus.subjectTime delay neural networks
fei.scopus.subjectTime series forecasting
fei.scopus.updated2024-05-01
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84943410330&origin=inward
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