A new hybrid neural network based on gated recurrent unit and simple exponential smoothing for forecasting

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 a...

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Vydané v:	Computing Ročník 107; číslo 10; s. 196
Hlavní autori:	Kolemen, Emine, Egrioglu, Erol, Bas, Eren, Turkmen, Mustafa
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Vienna Springer Vienna 01.10.2025 Springer Nature B.V
Predmet:	Algorithms Artificial Intelligence Artificial neural networks Computer Appl. in Administrative Data Processing Computer Communication Networks Computer Science Deep learning Evolutionary computation Forecasting Information Systems Applications (incl.Internet) Machine learning Neural networks Particle swarm optimization Regular Paper Smoothing Software Engineering Stream flow Time series Traffic flow Water resources Wind power Exponential smoothing Gated recurrent unit Differential evolution algorithm Forecasting Sustainable water resources Particle swarm optimization algorithm
ISSN:	0010-485X, 1436-5057
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Abstract	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.
AbstractList	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.
ArticleNumber	196
Author	Egrioglu, Erol Turkmen, Mustafa Kolemen, Emine Bas, Eren
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SubjectTerms	Algorithms Artificial Intelligence Artificial neural networks Computer Appl. in Administrative Data Processing Computer Communication Networks Computer Science Deep learning Evolutionary computation Forecasting Information Systems Applications (incl.Internet) Machine learning Neural networks Particle swarm optimization Regular Paper Smoothing Software Engineering Stream flow Time series Traffic flow Water resources Wind power
Title	A new hybrid neural network based on gated recurrent unit and simple exponential smoothing for forecasting
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