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المؤلفDu, Liang
المؤلفGao, Ruobin
المؤلفSuganthan, Ponnuthurai Nagaratnam
المؤلفWang, David Z.W.
تاريخ الإتاحة2023-02-13T08:14:07Z
تاريخ النشر2022-01-01
اسم المنشورProceedings of the International Joint Conference on Neural Networks
المعرّفhttp://dx.doi.org/10.1109/IJCNN55064.2022.9892044
الاقتباسDu, 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.‏
الترقيم الدولي الموحد للكتاب 9781728186719
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85140794721&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/40006
الملخصTime 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.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعcomponent
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العنوانTime Series Forecasting Using Online Performance-based Ensemble Deep Random Vector Functional Link Neural Network
النوعConference Paper
رقم المجلد2022-July
dc.accessType Abstract Only


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