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

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  • Eren Bas
  • Erol Eğrioğlu

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.

Suggested Citation

  • Eren Bas & Erol Eğrioğlu, 2023. "A new recurrent pi‐sigma artificial neural network inspired by exponential smoothing feedback mechanism," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 802-812, July.
    • Handle: RePEc:wly:jforec:v:42:y:2023:i:4:p:802-812
    DOI: 10.1002/for.2919
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    References listed on IDEAS

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    1. Alma Y. Alanis & Oscar D. Sanchez & Jesus G. Alvarez, 2021. "Time Series Forecasting for Wind Energy Systems Based on High Order Neural Networks," Mathematics, MDPI, vol. 9(10), pages 1-18, May.
    2. Lanbiao Liu & Chen Chen & Bo Wang, 2022. "Predicting financial crises with machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 871-910, August.
    3. Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2022. "Uncertainty and forecastability of regional output growth in the UK: Evidence from machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1049-1064, September.
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