Online dynamic ensemble deep random vector functional link neural network for forecasting
<p>This paper proposes a three-stage online deep learning model for time series based on the ensemble deep random vector functional link (edRVFL). The edRVFL stacks multiple randomized layers to enhance the single-layer RVFL’s representation ability. Each hidden layer’s representation is utili...
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2023
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| _version_ | 1864513549527678976 |
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| author | Ruobin Gao (16003195) |
| author2 | Ruilin Li (5627456) Minghui Hu (2457952) P.N. Suganthan (16518528) Kum Fai Yuen (16518531) |
| author2_role | author author author author |
| author_facet | Ruobin Gao (16003195) Ruilin Li (5627456) Minghui Hu (2457952) P.N. Suganthan (16518528) Kum Fai Yuen (16518531) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ruobin Gao (16003195) Ruilin Li (5627456) Minghui Hu (2457952) P.N. Suganthan (16518528) Kum Fai Yuen (16518531) |
| dc.date.none.fl_str_mv | 2023-09-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.neunet.2023.06.042 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Online_dynamic_ensemble_deep_random_vector_functional_link_neural_network_for_forecasting/23652789 |
| 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 Data management and data science Machine learning Forecasting Random vector functional link network Deep learning Machine learning Online learning Continual learning |
| dc.title.none.fl_str_mv | Online dynamic ensemble deep random vector functional link neural network for forecasting |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>This paper proposes a three-stage online deep learning model for time series based on the ensemble deep random vector functional link (edRVFL). The edRVFL stacks multiple randomized layers to enhance the single-layer RVFL’s representation ability. Each hidden layer’s representation is utilized for training an output layer, and the ensemble of all output layers forms the edRVFL’s output. However, the original edRVFL is not designed for online learning, and the randomized nature of the features is harmful to extracting meaningful temporal features. In order to address the limitations and extend the edRVFL to an online learning mode, this paper proposes a dynamic edRVFL consisting of three online components, the online decomposition, the online training, and the online dynamic ensemble. First, an online decomposition is utilized as a feature engineering block for the edRVFL. Then, an online learning algorithm is designed to learn the edRVFL. Finally, an online dynamic ensemble method, which can measure the change in the distribution, is proposed for aggregating all layers’ outputs. This paper evaluates and compares the proposed model with state-of-the-art methods on sixteen time series. </p> <h2>Other Information</h2> <p>Published in: Neural Networks<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br> See article on publisher's website: <a href="http://dx.doi.org/10.1016/j.neunet.2023.06.042" target="_blank">http://dx.doi.org/10.1016/j.neunet.2023.06.042 </a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_38ded4504b1bdd5c08582fd85c7baf64 |
| identifier_str_mv | 10.1016/j.neunet.2023.06.042 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/23652789 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Online dynamic ensemble deep random vector functional link neural network for forecastingRuobin Gao (16003195)Ruilin Li (5627456)Minghui Hu (2457952)P.N. Suganthan (16518528)Kum Fai Yuen (16518531)Information and computing sciencesArtificial intelligenceData management and data scienceMachine learningForecastingRandom vector functional link networkDeep learningMachine learningOnline learningContinual learning<p>This paper proposes a three-stage online deep learning model for time series based on the ensemble deep random vector functional link (edRVFL). The edRVFL stacks multiple randomized layers to enhance the single-layer RVFL’s representation ability. Each hidden layer’s representation is utilized for training an output layer, and the ensemble of all output layers forms the edRVFL’s output. However, the original edRVFL is not designed for online learning, and the randomized nature of the features is harmful to extracting meaningful temporal features. In order to address the limitations and extend the edRVFL to an online learning mode, this paper proposes a dynamic edRVFL consisting of three online components, the online decomposition, the online training, and the online dynamic ensemble. First, an online decomposition is utilized as a feature engineering block for the edRVFL. Then, an online learning algorithm is designed to learn the edRVFL. Finally, an online dynamic ensemble method, which can measure the change in the distribution, is proposed for aggregating all layers’ outputs. This paper evaluates and compares the proposed model with state-of-the-art methods on sixteen time series. </p> <h2>Other Information</h2> <p>Published in: Neural Networks<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br> See article on publisher's website: <a href="http://dx.doi.org/10.1016/j.neunet.2023.06.042" target="_blank">http://dx.doi.org/10.1016/j.neunet.2023.06.042 </a></p>2023-09-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.neunet.2023.06.042https://figshare.com/articles/journal_contribution/Online_dynamic_ensemble_deep_random_vector_functional_link_neural_network_for_forecasting/23652789CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/236527892023-09-01T00:00:00Z |
| spellingShingle | Online dynamic ensemble deep random vector functional link neural network for forecasting Ruobin Gao (16003195) Information and computing sciences Artificial intelligence Data management and data science Machine learning Forecasting Random vector functional link network Deep learning Machine learning Online learning Continual learning |
| status_str | publishedVersion |
| title | Online dynamic ensemble deep random vector functional link neural network for forecasting |
| title_full | Online dynamic ensemble deep random vector functional link neural network for forecasting |
| title_fullStr | Online dynamic ensemble deep random vector functional link neural network for forecasting |
| title_full_unstemmed | Online dynamic ensemble deep random vector functional link neural network for forecasting |
| title_short | Online dynamic ensemble deep random vector functional link neural network for forecasting |
| title_sort | Online dynamic ensemble deep random vector functional link neural network for forecasting |
| topic | Information and computing sciences Artificial intelligence Data management and data science Machine learning Forecasting Random vector functional link network Deep learning Machine learning Online learning Continual learning |