CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
Reseach Article

Classification based Expert Selection for Accurate Sales Forecasting

by Darshana D. Chande, M. Vijayalakshmi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 61 - Number 12
Year of Publication: 2013
Authors: Darshana D. Chande, M. Vijayalakshmi
10.5120/9982-4812

Darshana D. Chande, M. Vijayalakshmi . Classification based Expert Selection for Accurate Sales Forecasting. International Journal of Computer Applications. 61, 12 ( January 2013), 31-38. DOI=10.5120/9982-4812

@article{ 10.5120/9982-4812,
author = { Darshana D. Chande, M. Vijayalakshmi },
title = { Classification based Expert Selection for Accurate Sales Forecasting },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 61 },
number = { 12 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 31-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume61/number12/9982-4812/ },
doi = { 10.5120/9982-4812 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:08:56.625734+05:30
%A Darshana D. Chande
%A M. Vijayalakshmi
%T Classification based Expert Selection for Accurate Sales Forecasting
%J International Journal of Computer Applications
%@ 0975-8887
%V 61
%N 12
%P 31-38
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Forecasting methods used in practice vary from domain to domain. This Paper focuses on sales forecasting. Most of the series considered here are composed of three components-Trend, seasonality and irregular. A series has been decomposed into its three components and multiple forecasters (Experts) have been applied on each component. Then these forecasters are recombined, using Cartesian product of their forecasts, to generate a set of Experts. A classification based scheme is proposed to identify a final good set of Experts which can be used in various combinations to create forecast for each series. Further it has been demonstrated that this forecasting system succeeds in producing a forecast that is more accurate than the Holt Winter method, which is a standard method of forecasting.

References
  1. Brockwell, P. J. and Davis, R. A. (1991) Time series: Theory and Methods, 2ndEdn. Springer International Edition.
  2. Makridakis, S. , Wheelwright, S. , and Hyndman, R. (1998), Forecasting methods and Applications, 3rd Edition. Wiley: NY.
  3. Armstrong J. Scott, (2001), Combining Forecasts. Principles of Forecasting: A Handbook for Researchers and Practitioners, J. Scott Armstrong (ed. ): Norwell, MA: Lower Academic Publishers.
  4. Bates, J. M. , & Granger, C. W. J. (1969). Combination of forecasts. Operations Research Quarterly, 20: 451–468.
  5. "Performance comparison of Rule based Classification Algorithm" Prafulla Gupta, Durga Tosniwal, International Journal of Computer Science & informatics,Vol –I, 2011
  6. "Time-Series Classification based on Individualised Error Prediction" Krisztian Buza, Alexandros Nanopoulos, Lars Schmidt, 2010 13th IEEE International Conference on Computational Science and Engineering.
  7. Granger, C. , & Ramanathan, R. (1984), Improved methods of combining forecasts, Journal of Forecasting 3:197–204.
  8. Hand, D. J. (2009). Mining the past to determine the future: Problems and possibilities. International Journal of Forecasting, 25(3), 441–451.
  9. Clemens Robert T. (1989), Combining forecasts: A review and annotated bibliography, International Journal of Forecasting 5: 559-583.
  10. YaohuiBai, Jiancheng Sun, JianguoLuo and Xiaobin Zhang "Forecasting Financial Time Series with Ensemble Learning", 2010 International Symposium on Intelligent Signal Processing and Communication Systems (lSPACS 2010) December 6-8,2010 IEEE
  11. "Learning Weights for Linear Combination of Forecasting Methods", Ricardo B. C. Prudencio and Teresa B. Ludermir, Proceedings of the Ninth Brazilian Symposium on Neural Networks (SBRN'06)0-7695-2680-2/06 $20. 00 © 2006
  12. "Joint Segmentation and Classification of Time Series Using Class-Specific Features"Zhen Jane Wang and Peter Willett, Fellow, IEEE, ieee transactions on systems, man, and cybernetics—part b: cybernetics, vol. 34, no. 2, april 2004
  13. "The comparative study on linear and non-linear forecast-combination methods based on neural network" Han Dongmei1,2, Niu Wen-qing1, Yu Changrui1,, 1-4244-1312-5/07/$25. 00 © 2007 IEEE
  14. "Wind Power Ramp Events Classification and Forecasting: A Data Mining Approach" Hamidreza Zareipour, Dongliang Huang, William Rosehart, , 978-1-4577-1002-5/11/$26. 00 ©2011 IEEE
  15. Menezes B. , Seth A. , and Singh R. , (2007), Can a million Experts improve your sales' forecasts? European Symposium on Time Series Prediction, Helsinki, Finland, Feb. 7-9, 2007
  16. Zou H. and Yang Y. , (2004), "Combining time series models for forecasting" International Journal of Forecasting, 20 (1): 69-84.
  17. Timmermann, A. (2006), "Forecast Combinations," in eds. G. Elliott, C. W. J. Granger and A. Timmermann, Handbook of Economic Forecasting, Elsevier Press.
  18. Economagic. com: Economic time series page. http://www. economagic. com/.
  19. http://datamarket. com/data/list/? q=provider:tsdl granularity:monthly
  20. VenuGopal, (2007), Forecasting using consistent Experts, Master's Thesis, Kanwal Rekhi School of Information Technology, Bombay, INDIA.
Index Terms

Computer Science
Information Sciences

Keywords

Decomposition MAPE Classification Decision Tree Experts Combination