<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

Department of Biostatistics, Medical Faculty

Department of Statistics, Faculty of Arts and Science

Department of Econometrics, Faculty of Economic and Administrative Sciences

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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...

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

Online veröffentlicht: 01. Nov. 2017

Seitenbereich: 121 - 132

Eingereicht: 03. März 2017

Akzeptiert: 22. März 2017

DOI: https://doi.org/10.1515/jaiscr-2018-0009

Schlüsselwörter
High order artificial neural networks, pi-sigma neural network, forecasting, recurrent neural network, Particle Swarm Optimization

© 2018 Esra Akdeniz et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Online veröffentlicht: 01. Nov. 2017

Seitenbereich: 121 - 132

Eingereicht: 03. März 2017

Akzeptiert: 22. März 2017

DOI: https://doi.org/10.1515/jaiscr-2018-0009

SchlüsselwörterHigh order artificial neural networks, pi-sigma neural network, forecasting, recurrent neural network, Particle Swarm Optimization

© 2018 Esra Akdeniz et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
High order artificial neural networks, pi-sigma neural network, forecasting, recurrent neural network, Particle Swarm Optimization