An encoder–decoder architecture with Fourier attention for chaotic time series multi-step prediction

Multi-step prediction of chaotic time series has been a challenging problem. An encoder–decoder architecture based on novel Fourier attention was proposed, called FGNet, applied to chaotic time series multi-step prediction. Two perspectives were carefully re-considered in modeling, one was the integ...

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Veröffentlicht in:	Applied soft computing Jg. 156; S. 111409
Hauptverfasser:	Fu, Ke, Li, He, Shi, Xiaotian
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.05.2024
Schlagworte:	Chaotic time series Fourier transform Information fusion Multi-step prediction Self-attention model Fourier transform Chaotic time series Multi-step prediction Self-attention model Information fusion
ISSN:	1568-4946, 1872-9681
Online-Zugang:	Volltext
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