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Seasonal Time Series Forecasting Models based on Artificial Neural Network

by Benkachcha. S, Benhra. J, El Hassani. H
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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
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Index Terms

Computer Science
Information Sciences

Keywords

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

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