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2 Journal of Artificial Intelligence and Soft Computing Research

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

Akdeniz E., Egrioglu E., Bas E., Yolcu U.

Journal of Artificial Intelligence and Soft Computing Research

2018

Vol. 8, No. 2

121--132

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.

The training of multiplicative neuron model based artificial neural networks with differential evolution algorithm for forecasting

Bas E.

Journal of Artificial Intelligence and Soft Computing Research

2016

Vol. 6, No. 1

5--11

In recent years, artificial neural networks have been commonly used for time series forecasting by researchers from various fields. There are some types of artificial neural networks and feed forward artificial neural networks model is one of them. Although feed forward artificial neural networks gives successful forecasting results they have a basic problem. This problem is architecture selection problem. In order to eliminate this problem, Yadav et al. (2007) proposed multiplicative neuron model artificial neural network. In this study, differential evolution algorithm is proposed for the training of multiplicative neuron model for forecasting. The proposed method is applied to two well-known different real world time series data.