Stacked Ensemble Deep Random Vector Functional Link Network With Residual Learning for Medium-Scale Time-Series Forecasting
<p dir="ltr">The deep random vector functional link (dRVFL) and ensemble dRVFL (edRVFL) succeed in various tasks and achieve state-of-the-art performance compared with other randomized neural networks (NNs). However, existing edRVFL structures need more diversity and error correction...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2025
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| _version_ | 1864513538488270848 |
|---|---|
| author | Ruobin Gao (16003195) |
| author2 | Minghui Hu (2457952) Ruilin Li (5627456) Xuewen Luo (10012781) Ponnuthurai Nagaratnam Suganthan (11274636) M. Tanveer (1758181) |
| author2_role | author author author author author |
| author_facet | Ruobin Gao (16003195) Minghui Hu (2457952) Ruilin Li (5627456) Xuewen Luo (10012781) Ponnuthurai Nagaratnam Suganthan (11274636) M. Tanveer (1758181) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ruobin Gao (16003195) Minghui Hu (2457952) Ruilin Li (5627456) Xuewen Luo (10012781) Ponnuthurai Nagaratnam Suganthan (11274636) M. Tanveer (1758181) |
| dc.date.none.fl_str_mv | 2025-02-11T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/tnnls.2025.3529219 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Stacked_Ensemble_Deep_Random_Vector_Functional_Link_Network_With_Residual_Learning_for_Medium-Scale_Time-Series_Forecasting/30198277 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Machine learning Ensemble deep learning forecasting machine learning multiple output layers random vector functional link (RVFL) neural networks (NNs) randomized NNs transformers Artificial neural networks Training Feature extraction Vectors Forecasting Transformers Learning systems Uncertainty Stochastic processes Computational modeling |
| dc.title.none.fl_str_mv | Stacked Ensemble Deep Random Vector Functional Link Network With Residual Learning for Medium-Scale Time-Series Forecasting |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The deep random vector functional link (dRVFL) and ensemble dRVFL (edRVFL) succeed in various tasks and achieve state-of-the-art performance compared with other randomized neural networks (NNs). However, existing edRVFL structures need more diversity and error correction ability in an independent network. Our work fills the gap by combining stacked deep blocks and residual learning with the edRVFL. Subsequently, we propose a novel dRVFL combined with residual learning, ResdRVFL, whose deep layers calibrate the wrong estimations from shallow layers. Additionally, we propose incorporating a scaling parameter to control the scaling of residuals from shallow layers, thus mitigating the risk of overfitting. Finally, we present an ensemble deep stacking network, SResdRVFL, based on ResdRVFL. SResdRVFL aggregates multiple blocks into a cohesive network, leveraging the benefits of deep learning and ensemble learning. We evaluate the proposed model on 28 datasets and compare it with the state-of-the-art methods. The comparative study demonstrates that the SResdRVFL is the best-performing approach in terms of average ranking and errors based on 28 datasets.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Transactions on Neural Networks and Learning Systems<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/tnnls.2025.3529219" target="_blank">https://dx.doi.org/10.1109/tnnls.2025.3529219</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_3f76355a5c8308f0feab96ed0511efcf |
| identifier_str_mv | 10.1109/tnnls.2025.3529219 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30198277 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Stacked Ensemble Deep Random Vector Functional Link Network With Residual Learning for Medium-Scale Time-Series ForecastingRuobin Gao (16003195)Minghui Hu (2457952)Ruilin Li (5627456)Xuewen Luo (10012781)Ponnuthurai Nagaratnam Suganthan (11274636)M. Tanveer (1758181)Information and computing sciencesArtificial intelligenceMachine learningEnsemble deep learningforecastingmachine learningmultiple output layersrandom vector functional link (RVFL) neural networks (NNs)randomized NNstransformersArtificial neural networksTrainingFeature extractionVectorsForecastingTransformersLearning systemsUncertaintyStochastic processesComputational modeling<p dir="ltr">The deep random vector functional link (dRVFL) and ensemble dRVFL (edRVFL) succeed in various tasks and achieve state-of-the-art performance compared with other randomized neural networks (NNs). However, existing edRVFL structures need more diversity and error correction ability in an independent network. Our work fills the gap by combining stacked deep blocks and residual learning with the edRVFL. Subsequently, we propose a novel dRVFL combined with residual learning, ResdRVFL, whose deep layers calibrate the wrong estimations from shallow layers. Additionally, we propose incorporating a scaling parameter to control the scaling of residuals from shallow layers, thus mitigating the risk of overfitting. Finally, we present an ensemble deep stacking network, SResdRVFL, based on ResdRVFL. SResdRVFL aggregates multiple blocks into a cohesive network, leveraging the benefits of deep learning and ensemble learning. We evaluate the proposed model on 28 datasets and compare it with the state-of-the-art methods. The comparative study demonstrates that the SResdRVFL is the best-performing approach in terms of average ranking and errors based on 28 datasets.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Transactions on Neural Networks and Learning Systems<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/tnnls.2025.3529219" target="_blank">https://dx.doi.org/10.1109/tnnls.2025.3529219</a></p>2025-02-11T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/tnnls.2025.3529219https://figshare.com/articles/journal_contribution/Stacked_Ensemble_Deep_Random_Vector_Functional_Link_Network_With_Residual_Learning_for_Medium-Scale_Time-Series_Forecasting/30198277CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/301982772025-02-11T09:00:00Z |
| spellingShingle | Stacked Ensemble Deep Random Vector Functional Link Network With Residual Learning for Medium-Scale Time-Series Forecasting Ruobin Gao (16003195) Information and computing sciences Artificial intelligence Machine learning Ensemble deep learning forecasting machine learning multiple output layers random vector functional link (RVFL) neural networks (NNs) randomized NNs transformers Artificial neural networks Training Feature extraction Vectors Forecasting Transformers Learning systems Uncertainty Stochastic processes Computational modeling |
| status_str | publishedVersion |
| title | Stacked Ensemble Deep Random Vector Functional Link Network With Residual Learning for Medium-Scale Time-Series Forecasting |
| title_full | Stacked Ensemble Deep Random Vector Functional Link Network With Residual Learning for Medium-Scale Time-Series Forecasting |
| title_fullStr | Stacked Ensemble Deep Random Vector Functional Link Network With Residual Learning for Medium-Scale Time-Series Forecasting |
| title_full_unstemmed | Stacked Ensemble Deep Random Vector Functional Link Network With Residual Learning for Medium-Scale Time-Series Forecasting |
| title_short | Stacked Ensemble Deep Random Vector Functional Link Network With Residual Learning for Medium-Scale Time-Series Forecasting |
| title_sort | Stacked Ensemble Deep Random Vector Functional Link Network With Residual Learning for Medium-Scale Time-Series Forecasting |
| topic | Information and computing sciences Artificial intelligence Machine learning Ensemble deep learning forecasting machine learning multiple output layers random vector functional link (RVFL) neural networks (NNs) randomized NNs transformers Artificial neural networks Training Feature extraction Vectors Forecasting Transformers Learning systems Uncertainty Stochastic processes Computational modeling |