An ARMA Type Pi-Sigma Artificial Neural Network for Nonlinear Time Series Forecasting-Reference-Cited by-同舟云学术

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

Published:2017-11-01 Issue:2 Volume:8 Page:121-132
ISSN:2083-2567
Container-title:Journal of Artificial Intelligence and Soft Computing Research
language:en
Short-container-title:

Author:

Akdeniz Esra¹,Egrioglu Erol²,Bas Eren²,Yolcu Ufuk³

Affiliation:

1. Department of Biostatistics, Medical Faculty , Marmara University , Istanbul , Turkey

2. Department of Statistics, Faculty of Arts and Science , Forecast Research Laboratory , Giresun University , Giresun , 28100 , Turkey

3. Department of Econometrics, Faculty of Economic and Administrative Sciences , Forecast Research Laboratory , Giresun University , Giresun , 28100 , Turkey

Abstract

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.

Publisher

Walter de Gruyter GmbH

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Hardware and Architecture,Modelling and Simulation,Information Systems

Reference22 articles.

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