Abstract
Artificial neural network models have been frequently used in time series forecasting problems as an alternative to many classical forecasting models. Although multi-layer perceptron neural networks are one of the most frequently used artificial neural networks in the literature, high-order neural networks using high-order combinations of inputs have superior performance compared to multi-layer perceptron artificial neural networks in recent years. Although there are many high-order artificial neural networks with different properties in the literature, one of the most important problems of these high-order artificial neural networks is to determine the optimization method to be used in the training of the network structure. Sigma-Pi artificial neural networks, one of the high-order artificial neural networks, have been used frequently in many problems in recent years. Like many artificial neural networks, the training of the Sigma-Pi neural network is one of the important factors affecting the performance of the network. In this study, the grey wolf optimization algorithm is used for the first time in the literature in the training of Sigma-Pi artificial neural networks. Thus, a training process that does not require complex derivative calculations in derivative-based algorithms is performed. In the evaluation of the performance of the proposed method, the closing prices of the FTSE and S&P 500 are analyzed for different years. According to the analysis results, the proposed method has a 60% success rate for both FTSE and S&P 500 time series. For the comparison of all methods, the mean rank calculation is made for each method. The proposed method took first place in this ranking and is determined as the best method among all methods.
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The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
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Cansu Sarıkaya: Conceptualization, Methodology, Software, Writing- Original draft preparation, Writing- Reviewing and Editing, Visualization, Investigation, Data curation. Erol Egrioglu: Conceptualization, Methodology, Software, Writing- Original draft preparation, Writing- Reviewing and Editing, Visualization, Investigation, Data curation. Eren Bas: Conceptualization, Methodology, Software, Writing- Original draft preparation, Writing- Reviewing and Editing, Visualization, Investigation, Data curation.
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Sarıkaya, C., Bas, E. & Egrioglu, E. Training Sigma-Pi neural networks with the grey wolf optimization algorithm. Granul. Comput. 8, 981–989 (2023). https://doi.org/10.1007/s41066-023-00368-z
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DOI: https://doi.org/10.1007/s41066-023-00368-z