Forecasting international bandwidth capacity using linear and ANN methods

Gary Madden and Joachim Tan

Applied Economics, 2008, vol. 40, issue 14, 1775-1787

Abstract: An artificial neural network (ANN) can improve forecasts through pattern recognition of historical data. This article evaluates the reliability of ANN methods, as opposed to simple extrapolation techniques, to forecast Internet bandwidth index data that is bursty in nature. A simple feedforward ANN model is selected as a nonlinear alternative, as it is flexible enough to model complex linear or nonlinear relationships without any prior assumptions about the data generating process. These data are virtually white noise and provides a challenge to forecasters. Using standard forecast error statistics, the ANN and the simple exponential smoothing model provide modestly better forecasts than other extrapolation methods.

Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/00036840600905183 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Forecasting international bandwidth capacity using linear and ANN methods (2008)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:applec:v:40:y:2008:i:14:p:1775-1787

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEC20

DOI: 10.1080/00036840600905183

Access Statistics for this article

Applied Economics is currently edited by Anita Phillips

More articles in Applied Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().