Table of Contents Author Guidelines Submit a Manuscript
Advances in Operations Research
Volume 2014, Article ID 568478, 20 pages
http://dx.doi.org/10.1155/2014/568478
Research Article

Combining Diffusion Models and Macroeconomic Indicators with a Modified Genetic Programming Method: Implementation in Forecasting the Number of Mobile Telecommunications Subscribers in OECD Countries

Department of Electrical and Computer Engineering, Polytechnic Faculty, University of Patras, Rio Campus, 26504 Patras, Greece

Received 21 September 2013; Revised 20 February 2014; Accepted 28 April 2014; Published 16 June 2014

Academic Editor: Imed Kacem

Copyright © 2014 Konstantinos Salpasaranis et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. S. Armstrong, Principles of Forecasting: A Handbook for Researchers and Practitioners, Kluwer Academic Publishing, 2001.
  2. R. J. Hyndman and G. Athanasopoulos, “Forecasting: principles and practice,” 2012, https://www.otexts.org/fpp/.
  3. N. Meade and T. Islam, “Modelling and forecasting the diffusion of innovation—a 25-year review,” International Journal of Forecasting, vol. 22, no. 3, pp. 519–545, 2006. View at Publisher · View at Google Scholar · View at Scopus
  4. Z. Griliches, “Hybrid corn: an exploration in the economics of technological change,” Econometrica, vol. 25, no. 4, pp. 501–522, 1957. View at Google Scholar
  5. E. Mansfield, “Technical change and the rate of imitation,” Econometrica, vol. 29, pp. 741–766, 1961. View at Google Scholar
  6. F. M. Bass, “A new product growth for model consumer durables,” Management Science, vol. 15, no. 5, pp. 215–227, 1969. View at Google Scholar
  7. E. M. Rogers, Diffusion of Innovations, The Free Press, New York, NY, USA, 5th edition, 2003.
  8. S. Konstantinos and S. Vasilios, “A new empirical model for short-term forecasting of the broadband penetration: a short research in Greece,” Modelling and Simulation in Engineering, vol. 2011, Article ID 798960, 10 pages, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. K. Salpasaranis and V. Stylianakis, “A hybrid genetic programming method in optimization and forecasting: a case study of the broadband penetration in OECD countries,” Advances in Operations Research, vol. 2012, Article ID 904797, 32 pages, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. J. H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, 1975.
  11. J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, The MIT Press, 1992.
  12. J. R. Koza, “Genetic programming for economic modeling,” Statistics and Computing, vol. 4, no. 2, pp. 187–197, 1994. View at Google Scholar
  13. J. Duda and S. Szydło, “Collective intelligence of genetic programming for macroeconomic forecasting,” in Proceedings of the Computational Collective Intelligence. Technologies and Applications (ICCCI '11), vol. 6923 of Lecture Notes in Computer Science, pp. 445–454, Springer, Berlin, Germany, 2011.
  14. P. Meyer, “Bi-logistic growth,” Technological Forecasting and Social Change, vol. 47, no. 1, pp. 89–102, 1994. View at Google Scholar · View at Scopus
  15. P. S. Meyer and J. H. Ausubel, “Carrying capacity: a model with logistically varying limits,” Technological Forecasting and Social Change, vol. 61, no. 3, pp. 209–214, 1999. View at Publisher · View at Google Scholar · View at Scopus
  16. M. N. Sharif and K. Ramanathan, “Binomial innovation diffusion models with dynamic potential adopter population,” Technological Forecasting and Social Change, vol. 20, no. 1, pp. 63–87, 1981. View at Google Scholar · View at Scopus
  17. C. Chen and C. Watanabe, “Diffusion, substitution and competition dynamism inside the ICT market: the case of Japan,” Technological Forecasting and Social Change, vol. 73, no. 6, pp. 731–759, 2006. View at Publisher · View at Google Scholar · View at Scopus
  18. W. B. Langdon, R. Poli, N. F. McPhee, and J. R. Koza, “Genetic programming: an introduction and tutorial, with a survey of techniques and applications,” Studies in Computational Intelligence, vol. 115, pp. 927–1028, 2008. View at Publisher · View at Google Scholar · View at Scopus
  19. R. Kass and A. Raftery, “Bayes factors,” Journal of the American Statistical Association, vol. 90, pp. 773–795, 1995. View at Google Scholar
  20. OECD Factbook 2011, “Economic, Environmental and Social Statistics,” 2013. View at Publisher · View at Google Scholar
  21. GSMA, “European Mobile Industry Observatory,” 2011, London, UK, http://www.gsma.com/.
  22. OECD iLibrary, “OECD Communications Outlook,” 2011.