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Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

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2083-2567
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An ARMA Type Pi-Sigma Artificial Neural Network for Nonlinear Time Series Forecasting

Esra Akdeniz / Erol Egrioglu
  • Department of Statistics, Faculty of Arts and Science, Forecast Research Laboratory, Giresun University, Giresun, 28100, Turkey
  • Other articles by this author:
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/ Eren Bas
  • Department of Statistics, Faculty of Arts and Science, Forecast Research Laboratory, Giresun University, Giresun, 28100, Turkey
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Ufuk Yolcu
  • Department of Econometrics, Faculty of Economic and Administrative Sciences, Forecast Research Laboratory, Giresun University, Giresun, 28100, Turkey
  • Other articles by this author:
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Published Online: 2017-11-01 | DOI: https://doi.org/10.1515/jaiscr-2018-0009

Abstract

Real-life time series have complex and non-linear structures. Artificial Neural Networks have been frequently used in the literature to analyze non-linear time series. High order artificial neural networks, in view of other artificial neural network types, are more adaptable to the data because of their expandable model order. In this paper, a new recurrent architecture for Pi-Sigma artificial neural networks is proposed. A learning algorithm based on particle swarm optimization is also used as a tool for the training of the proposed neural network. The proposed new high order artificial neural network is applied to three real life time series data and also a simulation study is performed for Istanbul Stock Exchange data set.

Keywords: High order artificial neural networks; pi-sigma neural network; forecasting; recurrent neural network; Particle Swarm Optimization

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About the article

Received: 2017-03-03

Accepted: 2017-03-22

Published Online: 2017-11-01

Published in Print: 2018-04-01


Citation Information: Journal of Artificial Intelligence and Soft Computing Research, Volume 8, Issue 2, Pages 121–132, ISSN (Online) 2083-2567, DOI: https://doi.org/10.1515/jaiscr-2018-0009.

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© 2018 Esra Akdeniz et al., published by De Gruyter Open. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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