A Bi-GRU-based encoder–decoder framework for multivariate time series forecasting

Drought forecasting is crucial for minimizing the effects of drought, alerting people to its dangers, and assisting decision-makers in taking preventative action. This article suggests an encoder–decoder framework for multivariate times series (EDFMTS) forecasting. EDFMTS is composed of three layers...

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Veröffentlicht in:	Soft computing (Berlin, Germany) Jg. 28; H. 9-10; S. 6775 - 6786
Hauptverfasser:	Balti, Hanen, Ben Abbes, Ali, Farah, Imed Riadh
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2024 Springer Nature B.V
Schlagworte:	Accuracy Application of Soft Computing Artificial Intelligence Artificial neural networks Autoregressive models Big Data Climate change Coders Computational Intelligence Control Correlation coefficients Datasets Deep learning Drought Engineering Forecasting Forecasting techniques General circulation models Mathematical Logic and Foundations Mechatronics Multivariate analysis Neural networks Optimization Precipitation Rain Remote sensing Remote sensors Robotics Root-mean-square errors Sensors Support vector machines Time series Variables China Deep learning Encoder–decoder Multivariate time series Drought forecasting
ISSN:	1432-7643, 1433-7479
Online-Zugang:	Volltext
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