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AuthorDu, Liang
AuthorGao, Ruobin
AuthorSuganthan, Ponnuthurai Nagaratnam
AuthorWang, David Z.W.
Available date2023-02-13T08:14:07Z
Publication Date2022-01-01
Publication NameProceedings of the International Joint Conference on Neural Networks
Identifierhttp://dx.doi.org/10.1109/IJCNN55064.2022.9892044
CitationDu, L., Gao, R., Suganthan, P. N., & Wang, D. Z. (2022, July). Time Series Forecasting Using Online Performance-based Ensemble Deep Random Vector Functional Link Neural Network. In 2022 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). IEEE.‏
ISBN9781728186719
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85140794721&origin=inward
URIhttp://hdl.handle.net/10576/40006
AbstractTime series forecasting remains a challenging task in data science while it is of great relevance to decision-making in various industries such as transportation, finance, electricity resource management, meteorology. Traditional forecasting models based on statistics fail in challenging tasks with high non-linearity and complicated characteristics. Due to its architecture bias, deep learning-based models overfit randomness and noise. This paper proposes a novel online performance-based ensemble deep random vector functional link neural network model for the time series forecasting tasks. The proposed model supports the non-iterative online learning and dynamic ensemble method, which keeps adjusting the parameters and the weights of each output layer based on the dynamic evaluation of the latest prediction performance. Extensive experiments show that our proposed method outperforms the state-of-the-art statistical, machine learning-based, and deep learning-based models.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Subjectcomponent
formatting
insert
style
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TitleTime Series Forecasting Using Online Performance-based Ensemble Deep Random Vector Functional Link Neural Network
TypeConference Paper
Volume Number2022-July
dc.accessType Abstract Only


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