Recurrent ensemble random vector functional link neural network for financial time series forecasting
<p>Financial time series forecasting is crucial in empowering investors to make well-informed decisions, manage risks effectively, and strategically plan their investment activities. However, the non-stationary and non-linear characteristics inherent in time series data pose significant challe...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2024
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| الموضوعات: | |
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| _version_ | 1864513512944959488 |
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| author | Aryan Bhambu (18767731) |
| author2 | Ruobin Gao (16003195) Ponnuthurai Nagaratnam Suganthan (11274636) |
| author2_role | author author |
| author_facet | Aryan Bhambu (18767731) Ruobin Gao (16003195) Ponnuthurai Nagaratnam Suganthan (11274636) |
| author_role | author |
| dc.creator.none.fl_str_mv | Aryan Bhambu (18767731) Ruobin Gao (16003195) Ponnuthurai Nagaratnam Suganthan (11274636) |
| dc.date.none.fl_str_mv | 2024-08-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.asoc.2024.111759 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Recurrent_ensemble_random_vector_functional_link_neural_network_for_financial_time_series_forecasting/25974370 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Commerce, management, tourism and services Banking, finance and investment Time series forecasting Randomized neural network Machine learning Deep learning Ensemble deep learning Finance |
| dc.title.none.fl_str_mv | Recurrent ensemble random vector functional link neural network for financial time series forecasting |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Financial time series forecasting is crucial in empowering investors to make well-informed decisions, manage risks effectively, and strategically plan their investment activities. However, the non-stationary and non-linear characteristics inherent in time series data pose significant challenges when accurately predicting future forecasts. This paper proposes a novel Recurrent ensemble deep Random Vector Functional Link (RedRVFL) network for financial time series forecasting. The proposed model leverages randomly initialized and fixed weights for the recurrent hidden layers, ensuring stability during training. Furthermore, incorporating stacked hidden layers enables deep representation learning, facilitating the extraction of complex patterns from the data. The proposed model generates the forecast by combining the outputs of each layer through an ensemble approach. A comparative analysis was conducted against several state-of-the-art models over financial time-series datasets, and the results demonstrated the superior performance of our proposed model in terms of forecasting accuracy and predictive capability.</p><h2>Other Information</h2> <p> Published in: Applied Soft Computing<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.asoc.2024.111759" target="_blank">https://dx.doi.org/10.1016/j.asoc.2024.111759</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_bcdc91996806f3cd1f4396d039d6dc34 |
| identifier_str_mv | 10.1016/j.asoc.2024.111759 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25974370 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Recurrent ensemble random vector functional link neural network for financial time series forecastingAryan Bhambu (18767731)Ruobin Gao (16003195)Ponnuthurai Nagaratnam Suganthan (11274636)Commerce, management, tourism and servicesBanking, finance and investmentTime series forecastingRandomized neural networkMachine learningDeep learningEnsemble deep learningFinance<p>Financial time series forecasting is crucial in empowering investors to make well-informed decisions, manage risks effectively, and strategically plan their investment activities. However, the non-stationary and non-linear characteristics inherent in time series data pose significant challenges when accurately predicting future forecasts. This paper proposes a novel Recurrent ensemble deep Random Vector Functional Link (RedRVFL) network for financial time series forecasting. The proposed model leverages randomly initialized and fixed weights for the recurrent hidden layers, ensuring stability during training. Furthermore, incorporating stacked hidden layers enables deep representation learning, facilitating the extraction of complex patterns from the data. The proposed model generates the forecast by combining the outputs of each layer through an ensemble approach. A comparative analysis was conducted against several state-of-the-art models over financial time-series datasets, and the results demonstrated the superior performance of our proposed model in terms of forecasting accuracy and predictive capability.</p><h2>Other Information</h2> <p> Published in: Applied Soft Computing<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.asoc.2024.111759" target="_blank">https://dx.doi.org/10.1016/j.asoc.2024.111759</a></p>2024-08-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.asoc.2024.111759https://figshare.com/articles/journal_contribution/Recurrent_ensemble_random_vector_functional_link_neural_network_for_financial_time_series_forecasting/25974370CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/259743702024-08-01T00:00:00Z |
| spellingShingle | Recurrent ensemble random vector functional link neural network for financial time series forecasting Aryan Bhambu (18767731) Commerce, management, tourism and services Banking, finance and investment Time series forecasting Randomized neural network Machine learning Deep learning Ensemble deep learning Finance |
| status_str | publishedVersion |
| title | Recurrent ensemble random vector functional link neural network for financial time series forecasting |
| title_full | Recurrent ensemble random vector functional link neural network for financial time series forecasting |
| title_fullStr | Recurrent ensemble random vector functional link neural network for financial time series forecasting |
| title_full_unstemmed | Recurrent ensemble random vector functional link neural network for financial time series forecasting |
| title_short | Recurrent ensemble random vector functional link neural network for financial time series forecasting |
| title_sort | Recurrent ensemble random vector functional link neural network for financial time series forecasting |
| topic | Commerce, management, tourism and services Banking, finance and investment Time series forecasting Randomized neural network Machine learning Deep learning Ensemble deep learning Finance |