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Automatic neural network modeling for univariate time series

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Listed author(s):
  • Balkin, Sandy D.
  • Ord, J. Keith

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File URL: http://www.sciencedirect.com/science/article/pii/S0169-2070(00)00072-8
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Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 16 (2000)
Issue (Month): 4 ()
Pages: 509-515

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Handle: RePEc:eee:intfor:v:16:y:2000:i:4:p:509-515
Contact details of provider: Web page: http://www.elsevier.com/locate/ijforecast

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  1. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
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Cited by:

  1. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660.
  2. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
  3. Jane Binner & Rakesh Bissoondeeal & Thomas Elger & Alicia Gazely & Andrew Mullineux, 2004. "Vector autoregressive models versus neural networks in forecasting: an application to Euro-inflation and divisia money," Money Macro and Finance (MMF) Research Group Conference 2003 5, Money Macro and Finance Research Group.
  4. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
  5. Laura Brown & Saeed Moshiri, 2004. "Unemployment variation over the business cycles: a comparison of forecasting models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(7), pages 497-511.
  6. Qi, Min & Yang, Sha, 2003. "Forecasting consumer credit card adoption: what can we learn about the utility function?," International Journal of Forecasting, Elsevier, vol. 19(1), pages 71-85.
  7. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 672-688, July.
  8. Teddy, S.D. & Ng, S.K., 2011. "Forecasting ATM cash demands using a local learning model of cerebellar associative memory network," International Journal of Forecasting, Elsevier, vol. 27(3), pages 760-776, July.
  9. Ebrahimpour, Reza & Nikoo, Hossein & Masoudnia, Saeed & Yousefi, Mohammad Reza & Ghaemi, Mohammad Sajjad, 2011. "Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange," International Journal of Forecasting, Elsevier, vol. 27(3), pages 804-816, July.
  10. Rubio, Ginés & Pomares, Héctor & Rojas, Ignacio & Herrera, Luis Javier, 2011. "A heuristic method for parameter selection in LS-SVM: Application to time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 725-739, July.
  11. Goodwin, Paul & Ord, J. Keith & Oller, Lars-Erik & Sniezek, Janet A. & Leonard, Mike, 2002. "Principles of Forecasting: A Handbook for Researchers and Practitioners: J. Scott Armstrong (Ed.), (2001), Boston: Kluwer Academic Publishers, 849 pages. Hardback: ISBN: 0-7923-7930-6; $190, [UK pound," International Journal of Forecasting, Elsevier, vol. 18(3), pages 468-478.
  12. Jane Binner & Rakesh Bissoondeeal & Thomas Elger & Alicia Gazely & Andrew Mullineux, 2005. "A comparison of linear forecasting models and neural networks: an application to Euro inflation and Euro Divisia," Applied Economics, Taylor & Francis Journals, vol. 37(6), pages 665-680.
  13. Timo Teräsvirta & Marcelo C. Medeiros & Gianluigi Rech, 2006. "Building neural network models for time series: a statistical approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(1), pages 49-75.
  14. Ferbar Tratar, Liljana & Strmčnik, Ervin, 2016. "The comparison of Holt–Winters method and Multiple regression method: A case study," Energy, Elsevier, vol. 109(C), pages 266-276.

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