Table of Contents
Journal of Industrial Engineering
Volume 2017 (2017), Article ID 2061260, 8 pages
https://doi.org/10.1155/2017/2061260
Research Article

A Study of Time Series Model for Predicting Jute Yarn Demand: Case Study

1Department of Industrial and Production Engineering, Jessore University of Science and Technology, Jessore 7408, Bangladesh
2School of Engineering, Royal Melbourne Institute of Technology University, Melbourne, VIC 3001, Australia
3Hajee Mohammad Danesh Science and Technology University, Dinajpur 5200, Bangladesh

Correspondence should be addressed to P. K. Halder

Received 23 February 2017; Accepted 22 June 2017; Published 27 July 2017

Academic Editor: Gabor Szederkenyi

Copyright © 2017 C. L. Karmaker 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. Strasheim, “Demand forecasting for motor vehicle spare parts,” Journal of Industrial Engineering, vol. 6, no. 2, pp. 18-19, 1992. View at Publisher · View at Google Scholar
  2. C. Floros, “Forecasting the UK unemployment rate: model comparisons,” vol. 2, pp. 57–72, 2005.
  3. Q. Zhu, Y. Guo, and G. Feng, “Household energy consumption in China: Forecasting with BVAR model up to 2015,” in Proceedings of the 2012 5th International Joint Conference on Computational Sciences and Optimization, CSO 2012, pp. 654–659, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. J. E. Cox Jr. and D. G. Loomis, “Improving forecasting through textbooks: a 25 year review,” International Journal of Forecasting, vol. 22, no. 3, pp. 617–624, 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. K. Ryu and A. Sanchez, “The evaluation of forecasting methods at an institutional foodservice dining facility,” The Journal of Hospitality Financial Management, vol. 11, no. 1, pp. 27–45, 2013. View at Publisher · View at Google Scholar
  6. P. Wallström and A. Segerstedt, “Evaluation of forecasting error measurements and techniques for intermittent demand,” International Journal of Production Economics, vol. 128, no. 2, pp. 625–636, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Sanwlani and M. Vijayalakshmi, “Forecasting sales through time series clustering,” International Journal of Data Mining & Knowledge Management Process, vol. 3, no. 1, pp. 39–56, 2013. View at Publisher · View at Google Scholar
  8. M. M. Hossain and F. Abdulla, “Jute production in Bangladesh: a time series analysis,” Journal of Mathematics and Statistics, vol. 11, no. 3, pp. 93–98, 2015. View at Publisher · View at Google Scholar · View at Scopus
  9. R. Davies, T. Coole, and D. Osipyw, “The application of time series modelling and monte carlo simulation: forecasting volatile inventory requirements,” Applied Mathematics, vol. 05, no. 08, pp. 1152–1168, 2014. View at Publisher · View at Google Scholar
  10. J. L. Miller, C. S. McCahon, and B. K. Bloss, “Food production forecasting with simple time series models,” Hospitality Research Journal, vol. 14, p. 21, 1991. View at Google Scholar
  11. M. Matsumoto and A. Ikeda, “Examination of demand forecasting by time series analysis for auto parts remanufacturing,” Journal of Remanufacturing, vol. 5, no. 1, 2015. View at Publisher · View at Google Scholar
  12. S.-T. Li, S.-C. Kuo, Y.-C. Cheng, and C.-C. Chen, “A vector forecasting model for fuzzy time series,” Applied Soft Computing Journal, vol. 11, no. 3, pp. 3125–3134, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. L. R. Weatherford and S. E. Kimes, “A comparison of forecasting methods for hotel revenue management,” International Journal of Forecasting, vol. 19, no. 3, pp. 401–415, 2003. View at Publisher · View at Google Scholar · View at Scopus