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Article:A Homogeneous Ensemble of Artificial Neural Networks for Time Series Forecasting

by Ratnadip Adhikari, R. K. Agrawal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 32 - Number 7
Year of Publication: 2011
Authors: Ratnadip Adhikari, R. K. Agrawal
10.5120/3913-5505

Ratnadip Adhikari, R. K. Agrawal . Article:A Homogeneous Ensemble of Artificial Neural Networks for Time Series Forecasting. International Journal of Computer Applications. 32, 7 ( October 2011), 1-8. DOI=10.5120/3913-5505

@article{ 10.5120/3913-5505,
author = { Ratnadip Adhikari, R. K. Agrawal },
title = { Article:A Homogeneous Ensemble of Artificial Neural Networks for Time Series Forecasting },
journal = { International Journal of Computer Applications },
issue_date = { October 2011 },
volume = { 32 },
number = { 7 },
month = { October },
year = { 2011 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume32/number7/3913-5505/ },
doi = { 10.5120/3913-5505 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:18:32.295490+05:30
%A Ratnadip Adhikari
%A R. K. Agrawal
%T Article:A Homogeneous Ensemble of Artificial Neural Networks for Time Series Forecasting
%J International Journal of Computer Applications
%@ 0975-8887
%V 32
%N 7
%P 1-8
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Enhancing the robustness and accuracy of time series forecasting models is an active area of research. Recently, Artificial Neural Networks (ANNs) have found extensive applications in many practical forecasting problems. However, the standard backpropagation ANN training algorithm has some critical issues, e.g. it has a slow convergence rate and often converges to a local minimum, the complex pattern of error surfaces, lack of proper training parameters selection methods, etc. To overcome these drawbacks, various improved training methods have been developed in literature; but, still none of them can be guaranteed as the best for all problems. In this paper, we propose a novel weighted ensemble scheme which intelligently combines multiple training algorithms to increase the ANN forecast accuracies. The weight for each training algorithm is determined from the performance of the corresponding ANN model on the validation dataset. Experimental results on four important time series depicts that our proposed technique reduces the mentioned shortcomings of individual ANN training algorithms to a great extent. Also it achieves significantly better forecast accuracies than two other popular statistical models.

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Index Terms

Computer Science
Information Sciences

Keywords

Time Series Forecasting Artificial Neural Network Ensemble Backpropagation Training Algorithm ARIMA Support Vector Machine