An arma type pi-sigma artificial neural network for nonlinear time series forecasting

• Autorzy: Akdeniz E. , Egrioglu E. , Bas E. , Yolcu U.

Identyfikatory

DOI

10.1515/jaiscr-2018-0009

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

high order artificial neural networks; pi-sigma neural network, forecasting; recurrent neural network; particle swarm optimization (PSO)

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2018
• Tom: Vol. 8, No. 2
• Strony: 121--132
• Opis fizyczny: Bibliogr. 22 poz., rys.

Twórcy

autor

Akdeniz E.

• Department of Biostatistics, Medical Faculty, Marmara University, Istanbul, Turkey

autor

Egrioglu E.

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

autor

Bas E.

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

autor

Yolcu U.

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

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).