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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Veröffentlicht in:	Computing Jg. 107; H. 10; S. 196
Hauptverfasser:	Kolemen, Emine, Egrioglu, Erol, Bas, Eren, Turkmen, Mustafa
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Vienna Springer Vienna 01.10.2025 Springer Nature B.V
Schlagworte:	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
Online-Zugang:	Volltext
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