Madden, Gary G and Tan, Joachim (2008): Forecasting international bandwidth capacity using linear and ANN methods. Published in: Applied Economics No. 40 (2008): pp. 1775-1787.
Download (149kB) | Preview
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
|Item Type:||MPRA Paper|
|Original Title:||Forecasting international bandwidth capacity using linear and ANN methods|
|Keywords:||Forecasting; international bandwidth capacity|
|Subjects:||L - Industrial Organization > L9 - Industry Studies: Transportation and Utilities > L96 - Telecommunications|
|Depositing User:||Gary G Madden|
|Date Deposited:||09. Apr 2009 06:40|
|Last Modified:||22. Feb 2013 19:27|
Armstrong, J. and Collopy, F. (1992) Error measures for generalizing about forecast methods: empirical comparisons, International Journal of Forecasting, 8, 69–80.
De Gooijer, J. and Kumar, K. (1992) Some recent developments in non-linear time series modelling, testing and forecasting, International Journal of Forecasting, 8, 135–56.
Engle, R. (1982) Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation, Econometrica, 50, 987–1007. Fildes, R. (1992) The evaluation of extrapolative forecasting methods, International Journal of Forecasting, 8, 81–98.
Fildes, R., Hibon, M., Makridakis, S. and Meade, N. (1998) Generalising about univariate forecasting methods: further empirical evidence, International Journal of Forecasting, 14, 339–58.
Goodwin, P. and Lawton, R. (1999) On the asymmetry of the symmetric MAPE, International Journal of Forecasting, 15, 405–08.
Grambsch, P. and Stahel, W. (1990) Forecasting demand for special services, International Journal of Forecasting, 6, 53–64.
Granger, C. (1993) Strategies for modelling nonlinear timeseries relationships, Economic Record, 69, 233–8.
Granger, C. W. and Terasvirta, T. (1993) Modelling Nonlinear Economic Relationships, Oxford University Press, Oxford.
Hochberg, Y. (1988) A sharper bonferroni procedure for multiple tests of significance, Biometrika, 75, 800–2.
Hornik, K., Stinchcome, M. and White, H. (1989) Multilayer feedforward networks are universal approximators, Neural Networks, 2, 359–66.
Koehler, A. B. (2001) The asymmetry of the sAPE measure and other comments on the M3-competition, International Journal of Forecasting, 17, 537–84.
Kuan, C. and White, H. (1994) Artificial neural networks: an econometric perspective, Econometric Reviews, 13, 1–143.
Ljung, G. and Box, G. (1978) On a measure of lack of fit in time series models, Biometrika, 65, 297–303.
Madden, G. and Coble-Neal, G. (2005) Forecasting international bandwidth capacity, Journal of Forecasting 24, 299–309.
Makridakis, S., Chatfield, C., Hibon, M., Lawrence, M., Mills, T., Ord, K. and Simmons, L. F. (1993) The M-2 competition: a real-time judgmentally based forecasting study, International Journal of Forecasting, 9, 5–23.
Makridakis, S. and Hibon, M. (2000) The M3-competition: results, conclusions and implications, International Journal of Forecasting, 16, 451–76.
McLeod, A. and Li, W. (1983) Diagnostic checking ARMA time series models using squared-residual autocorrelation, Journal of Time Series Analysis, 4, 269–73.
Noll, A. (1991) Introduction to Telephones and Telephone Systems, 2nd edn, Artech House, Boston.
Opinix (2001) Internet traffic report. Available at http:// www.internettrafficreport.com.
Pankratz, A. (1983) Forecasting with Univariate Box– Jenkins Models: Concepts and Cases, John Wiley, New York.
Parzen, E. (1982) ARARMA models for time series analysis and forecasting, Journal of Forecasting, 1, 67–82.
Qi, M. and Zhang, P. (2001) An investigation of model selection criteria for neural network time series forecasting, European Journal of Operational Research, 132, 666–80.
Rissanen, J. (1987) Stochastic complexity and the MDL principle, Econometric Reviews, 6, 85–102.
Ramsey, J. (1969) Tests for specification errors in classical linear least-squares regression analysis, Journal of the Royal Statistical Society B, 31, 350–71.
Tsay, R. (1989) Testing and modelling threshold autoregressive processes, Journal of American Statistical Association, 84, 213–40.
Ville´n-Altamirano, M. (2001) Overview of ITU recommendations on traffic engineering, ITU-T Working Party 3/2 on Traffic Engineering Working Paper, ITU, Geneva.
Warner, B. and Mistra, M. (1996) Understanding neural networks as statistical tools, American Statistician, 50, 284–93.
White, H. (1989) Some asymptotic results for learning in single layer feedforward network models, Journal of the American Statistical Association, 84, 1003–13.
White, H. (1990) Connectionist nonparametric regression: multilayer feedforward networks can learn arbitrary mappings, Neural Networks, 3, 535–50.
Zhang, G. (2004) Neural Networks in Business Forecasting, Idea Group Publishing, London.
Zhang, G., Patuwo, B. and Hu, M. (1998)Forecasting with artificial neural networks: the state of the art, International Journal of Forecasting, 14, 35–62