A new hybrid neural network based on gated recurrent unit and simple exponential smoothing for forecasting - Kolemen, Emine; Egrioglu, Erol; Bas, Eren; Turkmen, Mustafa (Computing, 2025)
Deep recurrent artificial neural networks can be easily adapted to the forecasting problem due to the dynamic structure in their architecture. In this paper, two different hybrid neural network architectures based on gated recurrent units and exponential smoothing are proposed for forecasting. The architectures combine gated recurrent units and structures inspired by exponential smoothing models. In addition, training algorithms based on differential evolution and particle swarm optimization algorithms are proposed separately for training. Different strategies are used to solve overfitting and local optimum problems in these training algorithms. The performance of the proposed method is applied to sustainable water resources. The analysis results show that the proposed deep neural network methods have superior forecasting performance than many artificial neural networks.