Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0305-0483(01)00027-5
Title: A simple neural network for ARMA(p,q) time series
Authors: Hwarng, H.B. 
Ang, H.T.
Keywords: ARIMA
ARMA
Backpropagation
Box-Jenkins models
Experimental design
Forecasting
Simulation
Time series analysis
Issue Date: 2001
Source: Hwarng, H.B., Ang, H.T. (2001). A simple neural network for ARMA(p,q) time series. Omega 29 (4) : 319-333. ScholarBank@NUS Repository. https://doi.org/10.1016/S0305-0483(01)00027-5
Abstract: This study was designed: (a) to investigate a simple neural-network solution to forecasting the special class of time series corresponding to a wide range of ARMA(p,q) structures; (b) to study the significance of matching the input window size with the nature of time series. The study adopted a simulation approach in conjunction with an experimental design. It is discovered that a simple two-layered network, with proper input window size, is able to consistently outperform the multi-layer feedforward network and that the two-layered network is comparable to the Box-Jenkins modelling approach for a majority of the ARMA(p,q) time series studied and better than the Box-Jenkins modelling approach when the ARMA structure gets more complex and generates more variability. The results affirm that it is unnecessary to use multi-layer feedforward networks for this special class of linear time series and that the two-layered network can be a useful forecasting alternative to the widely popular Box-Jenkins model. © 2001 Elsevier Science Ltd.
Source Title: Omega
URI: http://scholarbank.nus.edu.sg/handle/10635/44968
ISSN: 03050483
DOI: 10.1016/S0305-0483(01)00027-5
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