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

Causal Method and Time Series Forecasting model based on Artificial Neural Network

by Benkachcha. S, Benhra. J, El Hassani. H
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 75 - Number 7
Year of Publication: 2013
Authors: Benkachcha. S, Benhra. J, El Hassani. H
10.5120/13126-0482

Benkachcha. S, Benhra. J, El Hassani. H . Causal Method and Time Series Forecasting model based on Artificial Neural Network. International Journal of Computer Applications. 75, 7 ( August 2013), 37-42. DOI=10.5120/13126-0482

@article{ 10.5120/13126-0482,
author = { Benkachcha. S, Benhra. J, El Hassani. H },
title = { Causal Method and Time Series Forecasting model based on Artificial Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { August 2013 },
volume = { 75 },
number = { 7 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = { 37-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume75/number7/13126-0482/ },
doi = { 10.5120/13126-0482 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:43:40.460846+05:30
%A Benkachcha. S
%A Benhra. J
%A El Hassani. H
%T Causal Method and Time Series Forecasting model based on Artificial Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 75
%N 7
%P 37-42
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This article discusses two methods of dealing with demand variability. First a causal method based on multiple regression and artificial neural networks have been used. The ANN is trained for different structures and the best is retained. Secondly a multilayer perceptron model for time series forecasting is proposed. Several learning rules used to adjust the ANN weights have been evaluated. The results show that the performances obtained by the two methods are very similar. The cost criterion is then used to choose the appropriate model.

References
  1. Kesten C. Green, J. Scott Armstrong 2012. Demand Forecasting: Evidence-based Methods. https://marketing. wharton. upenn. edu/profile/226/printFriendly.
  2. Gosasang, V. , Chan. , W. and KIATTISIN, S. 2011. A Comparison of Traditional and Neural Networks Forecasting Techniques for Container Throughput at Bangkok Port. The Asian Journal of Shipping and Logistics, Vol. 27, N° 3, pp. 463-482.
  3. Armstrong, J. S. 2012 , Illusions in Regression Analysis, International Journal of Forecasting, Vol. 28, p 689 - 694.
  4. Chase, Charles W. , Jr. , 1997. "Integrating Market Response Models in Sales Forecasting. " The Journal of Business Forecasting. Spring: 2, 27.
  5. Chen, K. Y. 2011. Combining linear and nonlinear model in forecasting tourism demand. Expert Systems with Applications, Vol. 38, p 10368–10376.
  6. Mitrea, C. A. , Lee, C. K. M. , WuZ. 2009. A Comparison between Neural Networks and Traditional Forecasting Methods: A Case Study". International Journal of Engineering Business Management, Vol. 1, No. 2, p 19-24.
  7. Daniel Ortiz-Arroyo, Morten K. Skov and Quang Huynh, "Accurate Electricity Load Forecasting With Artificial Neural Networks" , Proceedings of the 2005 International Conference on Computational Intelligence for Modelling, Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCAIAWTIC'05) , 2005
  8. Wilamowski B. M. 2011. Neural Network Architectures. Industrial Electronics Handbook, vol. 5 – Intelligent Systems, 2nd Edition, chapter 6, pp. 6-1 to 6-17, CRC Press.
  9. Zhang G. , Patuwo, B. E. , Hu, M. Y. 1998. Forecasting with artificial neural networks : The state of the art. International Journal of Forecasting. Vol. 14, , p 35–62.
  10. Norizan M. , Maizah H. A. , Suhartono, Wan M. A. 2012. Forecasting Short Term Load Demand Using Multilayer Feed-forward (MLFF) Neural Network Model. Applied Mathematical Sciences, Vol. 6, no. 108, p. 5359 - 5368
  11. Anandhi V. , ManickaChezian R. , ParthibanK. T. 2012 Forecast of Demand and Supply of Pulpwood using Artificial Neural Network. International Journal of Computer Science and Telecommunications, Vol. 3, Issue 6, June, pp. 35-38
  12. Wilamowski B. M. 2011 Neural Networks Learning. Industrial Electronics Handbook, vol. 5 – Intelligent Systems, 2nd Edition, chapter 11, pp. 11-1 to 11-18, CRC Press.
  13. Wilamowski B. M. , Yu H. 2010. Improved Computation for Levenberg Marquardt Training. IEEE Trans. on Neural Networks, vol. 21, no. 6, pp. 930-937.
Index Terms

Computer Science
Information Sciences

Keywords

Demand Forecasting Supply Chain Time Series Causal Method Multiple Regression Artificial Neural Networks (ANN).