AN ARMA TYPE PI-SIGMA ARTIFICIAL NEURAL NETWORK FOR NONLINEAR TIME SERIES FORECASTING

AKDENİZ E., EĞRİOĞLU E., BAŞ E., YOLCU U.

JOURNAL OF ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING RESEARCH, cilt.8, sa.2, ss.121-131, 2018 (ESCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 8 Sayı: 2
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1515/jaiscr-2018-0009
  • Dergi Adı: JOURNAL OF ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING RESEARCH
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus
  • Sayfa Sayıları: ss.121-131
  • Anahtar Kelimeler: High order artificial neural networks, pi-sigma neural network, forecasting, recurrent neural network, Particle Swarm Optimization, MODEL
  • Marmara Üniversitesi Adresli: Evet

Özet

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.