CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Seasonal Time Series Forecasting Models based on Artificial Neural Network

by Benkachcha. S, Benhra. J, El Hassani. H
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 116 - Number 20
Year of Publication: 2015
Authors: Benkachcha. S, Benhra. J, El Hassani. H
10.5120/20451-2805

Benkachcha. S, Benhra. J, El Hassani. H . Seasonal Time Series Forecasting Models based on Artificial Neural Network. International Journal of Computer Applications. 116, 20 ( April 2015), 9-14. DOI=10.5120/20451-2805

@article{ 10.5120/20451-2805,
author = { Benkachcha. S, Benhra. J, El Hassani. H },
title = { Seasonal Time Series Forecasting Models based on Artificial Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 116 },
number = { 20 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 9-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume116/number20/20451-2805/ },
doi = { 10.5120/20451-2805 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:57:40.182336+05:30
%A Benkachcha. S
%A Benhra. J
%A El Hassani. H
%T Seasonal Time Series Forecasting Models based on Artificial Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 116
%N 20
%P 9-14
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Forecasting is the starting point for drawing good strategies facing the demand variability in the increasingly complex and competitive today's markets. This article discusses two methods of dealing with demand variability in seasonal time series using artificial neural networks (ANN). First a multilayer perceptron model for time series forecasting is proposed. Several learning rules used to adjust the ANN weights have been evaluated. Secondly a causal method based on artificial neural networks, using the components of decomposed time series as input variables, has been used. The results show that ANNs yield almost the same accuracy with or without decomposition of the original time series.

References
  1. Benkachcha. S, Benhra. J, El Hassani. H, 2013. Causal Method and Time Series Forecasting model based on Artificial Neural Network. International Journal of Computer Applications. Vol. 75, No 7, p. 0975 – 8887.
  2. Kesten C. Green, J. Scott Armstrong 2012. Demand Forecasting: Evidence-based Methods. https://marketing. wharton. upenn. edu/profile/226/printFriendly.
  3. 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.
  4. Zhang G. P. 2003. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing Vol. 50, p 159–175.
  5. Shuai Wang, Lean Yu, Ling Tang, Shouyang Wang, 2011. A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China. Energy Vol. 36, p. 6542-6554.
  6. Coskun Hamzaçebi 2008. Improving artificial neural networks' performance in seasonal time series forecasting. Vol. 178, p 4550–4559.
  7. Tseng F. -M. , Yu H. -C. , Tzeng G. -H. 2002. Combining neural network model with seasonal time series ARIMA model. Technological Forecasting & Social Change Vol. 69, p. 71–87.
  8. 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.
  9. Daniel Ortiz-Arroyo, Morten K. Skov and Quang Huynh, 2005 . 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) ,
  10. Shuai Wang, Lean Yu, Ling Tang, Shouyang Wang. 2011. A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China. Energy Vol. 36, p. 6542–6554.
  11. 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.
  12. 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.
  13. 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
  14. Faraway J. , Chatfield C. 1998. Time series forecasting with neural networks: a comparative study using the airline data. Applied Statistics. Vol. 47 p. 231–250.
  15. Wilamowski B. M. , Yu H. 2010. Improved Computation for Levenberg Marquardt Training. IEEE Trans. on Neural Networks, vol. 21, no. 6, pp. 930-937.
  16. 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.
Index Terms

Computer Science
Information Sciences

Keywords

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

Powered by PhDFocusTM