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Discrete Dynamics in Nature and Society
Volume 2013 (2013), Article ID 802528, 10 pages
http://dx.doi.org/10.1155/2013/802528
Research Article

Modeling of a Small Transportation Company’s Start-Up with Limited Data during Economic Recession

1School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
2Business School, University of Edinburgh, Edinburgh EH8 9JS, UK

Received 9 October 2013; Revised 11 November 2013; Accepted 25 November 2013

Academic Editor: Wuhong Wang

Copyright © 2013 Xiaoping Fang 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. National Bureau of Statistics of China, “The second economic census data bulletin (no.1),” 2009, http://www.stats.gov.cn/tjsj/pcsj/jjpc/2jp/indexch.htm.
  2. National Development and Reform Commission, National Bureau of Statistics of China and China Federation of Logistics & Purchasing, “2008 National Logistics operation Situation Bulletin,” 2009, http://www.sdpc.gov.cn/jjyx/xdwl/t20090306_264999.htm.
  3. X. Chen, X. Wang, and D. D. Wu, “Credit risk measurement and early warning of SMEs: an empirical study of listed SMEs in China,” Decision Support Systems, vol. 49, no. 3, pp. 301–310, 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Ansell and J. Banasik, “Forecasting white goods in a recesion,” IMA Journal of Management Mathematics, vol. 6, no. 3, pp. 315–331, 1995. View at Publisher · View at Google Scholar · View at Scopus
  5. T. J. Fite, G. D. Taylor, J. S. Usher, J. R. English, and J. N. Roberts, “Forecasting freight demand using economic indices,” International Journal of Physical Distribution & Logistics Management, vol. 32, no. 4, pp. 299–308, 2002.
  6. S. Shen, T. Fowkes, T. Whiteing, and D. Johnson, “Econometric modelling and forecasting of freight transport demand in Great Britain,” in Proceedings of European Transport Conference, http://abstracts.aetransport.org/.
  7. L. Zhou, B. Heimann, and U. Clausen, “Forecasting a logistic service demand based on neural network,” in Proceedings of the International Conference on Service Systems and Service Management (ICSSSM '06), vol. 1, pp. 530–534, October 2006. View at Publisher · View at Google Scholar · View at Scopus
  8. J. Harris, Fuzzy Logic Applications in Engineering Science, Intelligent Systems, Control and Automation: Science and Engineering, Springer, Dordrecht, The Netherlands, 2006.
  9. B. G. S. Hardie, P. S. Fader, and M. Wisniewski, “An empirical comparison of new product trial forecasting models,” Journal of Forecasting, vol. 17, no. 3-4, pp. 209–229, 1998. View at Scopus
  10. P. McBurney, S. Parsons, and J. Green, “Forecasting market demand for new telecommunications services: an introduction,” Telematics and Informatics, vol. 19, no. 3, pp. 225–249, 2002. View at Publisher · View at Google Scholar · View at Scopus
  11. A. Mukherjee and V. Kadiyali, “Forecasting in rapidly changing environments: an application to the U.S. motion picture industry,” Cornell University Johnson School Research Paper Series 10-07.
  12. G. Madden, R. Cooper, and R. Fildes, “Theoretically-motivated long-term forecasting with limited data,” Working Paper 240, The MIT Center for Digital Business, 2008.
  13. C. Christodoulos, C. Michalakelis, and D. Varoutas, “Forecasting with limited data: combining ARIMA and diffusion models,” Technological Forecasting & Social Change, vol. 77, no. 4, pp. 558–565, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. K. C. Green and J. S. Armstrong, “Demand forecasting: evidence-based methods,” Working Paper, University of Pennsylvania Department of Marketing, 2005.
  15. J. Mostard, R. Teunter, and R. De Koster, “Forecasting demand for single-period products: a case study in the apparel industry,” European Journal of Operational Research, vol. 211, no. 1, pp. 139–147, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. H. Tong, “Nonlinear time series analysis since 1990: some personal reflections,” Acta Mathematicae Applicatae Sinica, vol. 18, no. 2, pp. 177–184, 2002. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  17. J. Z. Huang and H. Shen, “Functional coefficient regression models for non-linear time series: a polynomial spline approach,” Scandinavian Journal of Statistics, vol. 31, no. 4, pp. 515–534, 2004. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  18. Z. Cai, “Trending time-varying coefficient time series models with serially correlated errors,” Journal of Econometrics, vol. 136, no. 1, pp. 163–188, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  19. M. Barrow, Statistics for Economics, Accounting and Business Studies, Pearson Education, London, UK, 5th edition, 2009.
  20. J. A. Nelder and R. W. M. Wedderburn, “Generalized linear models,” Journal of the Royal Statistical Society, Series A, vol. 135, no. 3, pp. 370–384, 1972.
  21. D. N. Gujarati, Basic Econometrics, McGraw-Hill, New York, NY, USA, 4th edition, 2003.