Title: An empirical analysis of a neural network model for the time series forecasting of different industrial segments
Authors: Fábio Augusto Mollik Zoucas; Patrícia Belfiore
Addresses: Production Engineering, FEI University Center, Av. Humberto de Alencar Castelo Branco, 3972, Assunção, São Bernardo do Campo – SP, Zip code: 09850-901, Brazil ' Management Engineering Department, Federal University of ABC, Rua Teixeira da Silva, 426 – 143, São Paulo, SP, Zip code: 04002-031, Brazil
Abstract: 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.
Keywords: time delay neural networks; industrial segments; multi-layer perceptron; MLP; time series forecasting; production time series.
International Journal of Applied Decision Sciences, 2015 Vol.8 No.3, pp.261 - 283
Received: 02 Jan 2015
Accepted: 14 Jun 2015
Published online: 01 Oct 2015 *