1. bookVolume 8 (2018): Issue 2 (April 2018)
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30 Dec 2014
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access type Open Access

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

Published Online: 01 Nov 2017
Page range: 121 - 132
Received: 03 Mar 2017
Accepted: 22 Mar 2017
Journal Details
License
Format
Journal
First Published
30 Dec 2014
Publication timeframe
4 times per year
Languages
English

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

[1] R.N. Yadav, P.K. Kalra, J. John, Time series prediction with single multiplicative neuron model, Applied Soft Computing, 7, 2007, 1157-1163.Search in Google Scholar

[2] E. Egrioglu, C.H. Aladag, U. Yolcu, and E. Bas, Recurrent multiplicative neuron model artificial neural network for non-linear time series forecasting, Neural Processing Letters 41(2), 2015, 249-258.Search in Google Scholar

[3] O. Gundogdu, E. Egrioglu, C.H. Aladag, and U. Yolcu, Multiplicative neuron model artificial neural network based on gauss activation function, Neural Computing and Applications 27(4), 2015, 927-935Search in Google Scholar

[4] D.E. Rumelhart, and J.L. Mcclelland, Parallel distributed processing: explorations in the microstructure of cognition, Cambridge (Britian): MIT Press, 1986.Search in Google Scholar

[5] C.L. Giles, and T. Maxwell, Learning, invariance, and generalization in a high-order neural network, Appl Opt, 26(23), 1978, 4972–8.Search in Google Scholar

[6] R. Durbin, and D.E. Rumelhart, Product units: a computationally powerful and biologically plausible extension to back propagation networks, Neural Computation, 1, 1989:133–42.Search in Google Scholar

[7] Y. Shin, and J. Gosh, The Pi-sigma Network: An efficient higher-order neural network for pattern classification and function approximation. In Proceedings of the International Joint Conference on Neural Networks, 1991.Search in Google Scholar

[8] R. Ghazali. A. Husaini, L.H. Ismail, T. Herawan, and Y.M. Hassim, The performance of a recurrent HONN for temperature time series prediction, 2014 International Joint Conference on Neural Networks (IJCNN), July 6-11, Proceeding Book, page 518-524, Beijing, China, 2014.Search in Google Scholar

[9] R. Ghazali. A. Husaini, and W. El-Deredy, Application of ridge polynomial neural networks to financial time series prediction. In: 2006 International joint conference on neural networks; July, 16–21, 2006, 913–20.Search in Google Scholar

[10] R. Ghazali, A.J. Hussain, P. Liatsis, and H. Tawfik, The application of ridge polynomial neural network to multi-step ahead financial time series prediction, Neural Computing & Applications, 17(3), 2008, 311–323.Search in Google Scholar

[11] H. Tawfik, and P. Liatsis, Prediction of non-linear time-series using higher-order neural networks, Proceeding IWSSIP’97 Conference, Poznan, Poland, 1977.Search in Google Scholar

[12] N. Yong, and D. Wei, A hybrid genetic learning algorithm for Pi– sigma neural network and the analysis of its convergence, In: IEEE fourth international conference on natural computation, 19–23, 2008Search in Google Scholar

[13] J. Nayak, B. Naik, and H.S. Behera, A hybrid PSO-GA based Pi sigma neural network (PSNN) with standard back propagation gradient descent learning for classification. International Conference on Control, Instrumentation, Communication and Computational Technologies, ICCICCT 2014, art. no. 6993082, 878-885, 2014b.Search in Google Scholar

[14] J. Nayak, B. Naik, and H.S. Behera, and A. Abraham, Particle swarm optimization based higher order neural network for classification, Smart Innovation, Systems and Technologies, 31, 2015, 401-414.Search in Google Scholar

[15] L. Chien-Kuo, Memory-based Sigma–Pi–Sigma neural network, IEEE SMC, TP1F5; 2002, 112–8.Search in Google Scholar

[16] A.J. Hussain, and P. Liatsis, Recurrent Pi–Sigma networks for DPCM image coding, Neurocomputing, 55, 2002, 363–82.Search in Google Scholar

[17] J. Nayak, D.P. Kanungo, B. Naik, and H.S. Behera, A higher order evolutionary Jordan Pi-sigma neural network with gradient descent learning for classification, 2014 International Conference on High Performance Computing and Applications, ICHPCA 2014, Article number 7045328.Search in Google Scholar

[18] J. Kennedy, R. Eberhart, Particle swarm optimization, In Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, USA, IEEE Press., 1995, 1942–1948.Search in Google Scholar

[19] C.H. Aladag, U. Yolcu, and E. Egrioglu, A new multiplicative seasonal neural network model based on particle swarm optimization, Neural Processing Letters 37(3), 2013, 251-262.10.1007/s11063-012-9244-yOpen DOISearch in Google Scholar

[20] G. Janacek, Practical time series. Oxford University Press Inc., New York, 156, 2001.Search in Google Scholar

[21] U. Yolcu, E. Egrioglu, C.H. Aladag, A new linear & nonlinear artificial neural network model for time series forecasting, Decision Support Systems, 2013, 1340–1347.10.1016/j.dss.2012.12.006Open DOISearch in Google Scholar

[22] J.L. Elman, Finding structure in time, Cognitive Science, 14 (2), 1990, 179–211.10.1207/s15516709cog1402_1Open DOISearch in Google Scholar

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