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IJMLC 2021 Vol.11(4): 274-280 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.4.1047

Tuning Parameters in Deep Belief Networks for Time Series Prediction through Harmony Search

Do Ngoc Luu, Nguyen Ngoc Phien, and Duong Tuan Anh

Abstract—There have been several researches of applying Deep Belief Networks (DBNs) to predict time series data. Most of these works pointed out that DBNs can bring out better prediction accuracy than traditional Artificial Neural Networks. However, one of the main shortcomings of using DBNs in time series prediction concerns with the proper selection of their parameters. In this paper, we investigate the use of Harmony Search algorithm for determining the parameters of DBN in forecasting time series. Experimental results on several synthetic and real world time series datasets revealed that the DBN with parameters selected by Harmony Search performs better than the DBN with parameters selected by Particle Swarm Optimization (PSO) or random method in most of the tested datasets.

Index Terms—Deep belief network, parameters, time series prediction, harmony search.

D. N. Luu was with the Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology, Vietnam (e-mail: luudnbktp@ gmail.com).
N. N. Phien is with Center for Applied Information Technology and Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam (e-mail: nguyenngocphien@tdtu.edu.vn).
D. T. Anh is with the Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology, Vietnam (e-mail: dtanh@hcmut.edu.vn).

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Cite: Do Ngoc Luu, Nguyen Ngoc Phien, and Duong Tuan Anh, "Tuning Parameters in Deep Belief Networks for Time Series Prediction through Harmony Search," International Journal of Machine Learning and Computing vol. 11, no. 4, pp. 274-280, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

 

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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