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Time series forecasting by using a neural arima model based on wavelet decomposition

Independent Journal of Management & Production

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Title Time series forecasting by using a neural arima model based on wavelet decomposition
 
Creator Pereira, Eliete Nascimento
Scarpin, Cassius Tadeu
Teixeira Júnior, Luíz Albino
 
Subject Production Engineering; Numeric Methods; Statistics
Wavelet decomposition, ARIMA model, Artificial neural networks, Linear combination of forecasts
 
Description In the prediction of (stochastic) time series, it has been common to suppose that an individual predictive method – for instance, an Auto-Regressive Integrated Moving Average (ARIMA) model – produces residuals like a white noise process. However, mainly due to the structures of auto-dependence not mapped by a given individual predictive method, this assumption may easily be violated, in practice, as pointed out in Firmino et al. (2015). In order to correct it (and accordingly to produce more forecasts with more accuracy power), this paper puts forward a Wavelet Hybrid Forecaster (WHF) that integrates the following numerical techniques: wavelet decomposition; ARIMA models; Artificial Neural Networks (ANNs); and linear combination of forecasts. Basically, the proposed WHF can map simultaneously linear – by means of a linear combination of ARIMA forecasts – and non-linear – through a linear combination of ANN forecasts – auto-dependence structures exhibited by a given time series. Differently of other hybrid methodologies existing in literature, the WHF forecasts are produced carrying into account implicitly the information from the frequency presenting in the underlying time series by means of the Wavelet Components (WCs) obtained by the wavelet decomposition approach. All numerical results show that WHF method has achieved remarkable accuracy gains, when comparing with other competitive forecasting methods already published in specialized literature, in the prediction of a well-known annual time series of sunspot.
 
Publisher Instituto Federal de Educação, Ciência e Tecnologia de São Paulo
 
Contributor Fundação Araucária
Capes
Universidade Federal do Paraná (UFPR)
Universidade Estadual do Oeste do Paraná (Unioeste)
Ceasb
 
Date 2016-03-01
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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Format application/pdf
text/html
 
Identifier http://www.ijmp.jor.br/index.php/ijmp/article/view/400
10.14807/ijmp.v7i1.400
 
Source Independent Journal of Management & Production; Vol 7, No 1 (2016): Independent Journal of Management & Production; 252-270
2236-269X
 
Language eng
 
Relation http://www.ijmp.jor.br/index.php/ijmp/article/view/400/287
http://www.ijmp.jor.br/index.php/ijmp/article/view/400/505
http://www.ijmp.jor.br/index.php/ijmp/article/downloadSuppFile/400/159
http://www.ijmp.jor.br/index.php/ijmp/article/downloadSuppFile/400/162
 
Rights Copyright (c) 2016 Eliete Nascimento Pereira, Cassius Tadeu Scarpin, Luíz Albino Teixeira Júnior
http://creativecommons.org/licenses/by/4.0