A Novel Approach for Reconstruction of IMFs of Decomposition and Ensemble Model for Forecasting of Crude Oil Prices

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aut.relation.endpage34207
aut.relation.issue99
aut.relation.journalIEEE Access
aut.relation.startpage34192
aut.relation.volume12
dc.contributor.authorNaeem, Muhammad
dc.contributor.authorAamir, Muhammad
dc.contributor.authorYu, Jian
dc.contributor.authorAlbalawi, Olayan
dc.date.accessioned2024-03-12T01:29:07Z
dc.date.available2024-03-12T01:29:07Z
dc.date.issued2024-02-26
dc.description.abstractIn recent eras, the complexity and fluctuations of the global crude oil prices have affected the economic progress of society. It is therefore, the oil price prediction has hauled the attention of scholars and policymakers. Driven by this critical concern for forecasting of crude oil prices, we introduces a novel hybrid model keeping in mind the primary objective of enhancing prediction accuracy while considering the specific characteristics as inherent in the data. To achieve this achievement, the trend is eliminated, allowing the scrutiny of whether the residual component validates the assurance of a series ran by stochastic trends. Following the removal of the trend, the residual component undergoes rigorous evaluation through autoregressive model following the decomposition model. Then we got support from the support vector machine, autoregressive integrated moving average and long-short term memory. The predictions accuracy can be evaluated by using the various performance metrics. The proposed hybrid model’s robustness and forecasting performance are rigorously evaluated through Diebold-Mariano test in comparison to competing models. Furthermore, the forecasting ability is evaluated via directional forecast. Ultimately, the empirical findings explicitly determine the superior predictive capabilities of the proposed hybrid model over alternative approaches.
dc.identifier.citationIEEE Access, ISSN: 2169-3536 (Print); 2169-3536 (Online), Institute of Electrical and Electronics Engineers (IEEE), 12(99), 34192-34207. doi: 10.1109/access.2024.3370440
dc.identifier.doi10.1109/access.2024.3370440
dc.identifier.issn2169-3536
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/10292/17319
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urihttps://ieeexplore.ieee.org/document/10445351
dc.rights© 2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrightsOpenAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject46 Information and Computing Sciences
dc.subject40 Engineering
dc.subject08 Information and Computing Sciences
dc.subject09 Engineering
dc.subject10 Technology
dc.subject40 Engineering
dc.subject46 Information and computing sciences
dc.titleA Novel Approach for Reconstruction of IMFs of Decomposition and Ensemble Model for Forecasting of Crude Oil Prices
dc.typeJournal Article
pubs.elements-id541262
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