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Time Series Forecasting using Evolutionary Neural Network

by Sibarama Panigrahi, Yasobanta Karali, H. S. Behera
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 75 - Number 10
Year of Publication: 2013
Authors: Sibarama Panigrahi, Yasobanta Karali, H. S. Behera
10.5120/13146-0553

Sibarama Panigrahi, Yasobanta Karali, H. S. Behera . Time Series Forecasting using Evolutionary Neural Network. International Journal of Computer Applications. 75, 10 ( August 2013), 13-17. DOI=10.5120/13146-0553

@article{ 10.5120/13146-0553,
author = { Sibarama Panigrahi, Yasobanta Karali, H. S. Behera },
title = { Time Series Forecasting using Evolutionary Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { August 2013 },
volume = { 75 },
number = { 10 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = { 13-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume75/number10/13146-0553/ },
doi = { 10.5120/13146-0553 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:43:54.680232+05:30
%A Sibarama Panigrahi
%A Yasobanta Karali
%A H. S. Behera
%T Time Series Forecasting using Evolutionary Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 75
%N 10
%P 13-17
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Efficient time series forecasting (TSF) is of utmost importance in order to make better decision under uncertainty. Over the past few years a large literature has evolved to forecast time series using different artificial neural network (ANN) models because of its several distinguishing characteristics. This paper evaluates the effectiveness of three methods to forecast time series, one carried out with ANN-GD using extended back propagation (EBP) algorithm, second one carried out with ANN-GA using genetic algorithm (GA) and the last one carried out with ANN-DE using differential evolution (DE). For comparative performance analysis between these methods two benchmark time series such as: wisconsin employment time series and monthly milk production time series are considered. Results show that both the ANN-GA and ANN-DE outperform ANN-GD considering forecast accuracy. It is also observed that the ANN-DE performs better than ANN-GA for both the time series considered.

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Index Terms

Computer Science
Information Sciences

Keywords

Time Series Forecasting Artificial Neural Network Differential Evolution Genetic Algorithm Extended Back Propagation Algorithm.

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