Please use this identifier to cite or link to this item:
|Title:||A simple neural network for ARMA(p,q) time series|
|Authors:||Hwarng, H.B. |
Time series analysis
|Citation:||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.|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 23, 2019
WEB OF SCIENCETM
checked on Jan 15, 2019
checked on Jan 12, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.