A new recurrent pi‐sigma artificial neural network inspired by exponential smoothing feedback mechanism

Eren Bas and Erol Eğrioğlu

Journal of Forecasting, 2023, vol. 42, issue 4, 802-812

Abstract: Pi‐sigma artificial neural networks have very good performance for forecasting problems because of their highly nonlinear model structure. Some time series can be forecasted better with the combination of simple and highly nonlinear structures. In this study, the architecture of the pi‐sigma artificial neural network is modified by an inspiring exponential smoothing feedback mechanism. The architecture of the proposed neural network automatically balances simple and complex nonlinear model structures. Moreover, a training algorithm is created by using the sine cosine algorithm. The training algorithm has some solutions for overfitting and local optimum problems. The main contribution of the study is to propose a new hybrid recurrent neural network and its training algorithm. In the analysis of the new network's performance, the randomly selected eight subseries of the Financial Times Stock Exchange 100 Index daily time series between 2014 and 2018 are used. The performance of the proposed method is compared with popular deep learning artificial neural networks, pi‐sigma artificial neural networks, and classical exponential smoothing methods. The statistical results show that the proposed method can produce better forecasting results than the others.

Date: 2023
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https://doi.org/10.1002/for.2919

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:42:y:2023:i:4:p:802-812

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