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

Fu, Ke; Li, He; Shi, Xiaotian

doi:10.1016/j.asoc.2024.111409

Applied Soft Computing

2024

DOI: 10.1016/j.asoc.2024.111409

|View full text |Cite

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

Ke Fu,

He Li,

Xiaotian Shi

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Multidimensional dynamic attention for multivariate time series forecasting

Almaghrabi,

Rana,

Hamilton

et al. 2024

Applied Soft Computing

View full text Add to dashboard Cite

Multidimensional dynamic attention for multivariate time series forecasting

Almaghrabi,

Rana,

Hamilton

et al. 2024

Applied Soft Computing

View full text Add to dashboard Cite

Many-to-many: Domain adaptation for water quality prediction

Wang,

Gao,

et al. 2024

Applied Soft Computing

View full text Add to dashboard Cite

An enhanced model based on deep operator network for very short-term forecasting of ship motion

Zhao,

Zhao

2024

Physics of Fluids

View full text Add to dashboard Cite

Very short-term forecasting of ship motion involves forecasting future ship movements based on learned characteristics from historical motion data. However, ship motion exhibits not only temporal features but also features in the frequency domain, which are often overlooked. This paper introduces a novel method called Fourier Transform and Multilayer Perceptron-net enhancement based on Deep Operator Network (DeepONet), abbreviated as FMD. This approach effectively captures and learns ship motion patterns in both the temporal and frequency domains. Specifically, the branch net of DeepONet learns temporal features, while the trunk net performs Fourier filtering to capture the underlying ship motion patterns. In addition, the learning effectiveness of Fourier filtering is complemented by using MPL-net to enhance the extraction of detailed features in motion data. To evaluate the prediction performance of FMD, this study explores the optimal filtering frequency of the FMD model using experimental ship model motion data. Comparative testing with the DeepONet model includes multi-step prediction, coupled data forecasting, and generalization studies. Testing results demonstrate that the proposed FMD model improves prediction accuracy from 11.78% to 33.10% under Mean Squared Error (MSE) compared to the DeepONet model. Even under sea conditions ranging from mild to intense, the FMD model maintains high accuracy, with an improvement of over 30% in accuracy compared to DeepONet at longer step lengths under MSE conditions. Testing results indicate the superiority and advancement of FMD in prediction accuracy, generalization, and versatility, showcasing significant advantages in very short-term forecasting of ship motion.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

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

Cited by 3 publications

References 33 publications

Multidimensional dynamic attention for multivariate time series forecasting

Multidimensional dynamic attention for multivariate time series forecasting

Many-to-many: Domain adaptation for water quality prediction

An enhanced model based on deep operator network for very short-term forecasting of ship motion

Contact Info

Product

Resources

About