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<p>Time-series forecasting, prediction, and estimation have been active research topics in recent years due to their critical role in decision-making and future planning. Artificial neural networks (ANNs) are widely employed for forecasting applications and have demonstrated significant performance improvements. In this paper, we utilize a specialized type of ANN, the dendritic neuron model (DNM), for time-series forecasting. We apply an optimized DNM model by utilizing the power of metaheuristic (MH) optimization algorithms. Specifically, we employ an MH algorithm, the mountain gazelle optimizer (MGO), to optimize the parameters of the DNM, improve its configuration process, and ultimately improve the accuracy of the forecast. Furthermore, we compare MGO performance with several other optimization algorithms used to optimize the DNM model. Comprehensive evaluations are conducted on four publicly available time series datasets using multiple evaluation indicators. The results demonstrate the superiority of the MGO-DNM model over competing methods, with optimized DNM configurations consistently outperforming the traditional DNM in forecasting tasks.</p>

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Dendritic neuron model optimized with mountain gazelle optimizer for time-series forecasting

  • 期刊论文

  • 发表日期：

    2025年8月6日

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  • Mohammed A. A. Al-qaness,
  • Ahmed A. Ewees,
  • Mohamed Abd Elaziz,
  • Robertas Damaševičius,
  • Ahmed H. Samak

摘要

Time-series forecasting, prediction, and estimation have been active research topics in recent years due to their critical role in decision-making and future planning. Artificial neural networks (ANNs) are widely employed for forecasting applications and have demonstrated significant performance improvements. In this paper, we utilize a specialized type of ANN, the dendritic neuron model (DNM), for time-series forecasting. We apply an optimized DNM model by utilizing the power of metaheuristic (MH) optimization algorithms. Specifically, we employ an MH algorithm, the mountain gazelle optimizer (MGO), to optimize the parameters of the DNM, improve its configuration process, and ultimately improve the accuracy of the forecast. Furthermore, we compare MGO performance with several other optimization algorithms used to optimize the DNM model. Comprehensive evaluations are conducted on four publicly available time series datasets using multiple evaluation indicators. The results demonstrate the superiority of the MGO-DNM model over competing methods, with optimized DNM configurations consistently outperforming the traditional DNM in forecasting tasks.