A multi-modal approach for mixed-frequency time series forecasting

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Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade Federal de Lavras (UFLA)-
Autor(es): dc.creatorFilho, Leopoldo Lusquino-
Autor(es): dc.creatorde Oliveira Werneck, Rafael-
Autor(es): dc.creatorCastro, Manuel-
Autor(es): dc.creatorRibeiro Mendes Júnior, Pedro-
Autor(es): dc.creatorLustosa, Augusto-
Autor(es): dc.creatorZampieri, Marcelo-
Autor(es): dc.creatorLinares, Oscar-
Autor(es): dc.creatorMoura, Renato-
Autor(es): dc.creatorMorais, Elayne-
Autor(es): dc.creatorAmaral, Murilo-
Autor(es): dc.creatorSalavati, Soroor-
Autor(es): dc.creatorLoomba, Ashish-
Autor(es): dc.creatorEsmin, Ahmed-
Autor(es): dc.creatorGonçalves, Maiara-
Autor(es): dc.creatorSchiozer, Denis José-
Autor(es): dc.creatorFerreira, Alexandre-
Autor(es): dc.creatorDavólio, Alessandra-
Autor(es): dc.creatorRocha, Anderson-
Data de aceite: dc.date.accessioned2025-08-21T21:33:35Z-
Data de disponibilização: dc.date.available2025-08-21T21:33:35Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s00521-024-10305-z-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/306430-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/306430-
Descrição: dc.descriptionThis study proposes a novel multimodal approach for mixed-frequency time series forecasting in the oil industry, enabling the use of high-frequency (HF) data in their original frequency. We specifically address the challenge of integrating HF data streams, such as pressure and temperature measurements, with daily time series without introducing noise. Our approach was compared with existing econometric regression model mixed-data sampling (MIDAS) and with the data-driven models N-HiTS and a GRU-based network, across short-, medium-, and long-term prediction horizons. Additionally, we validated the proposed method on datasets from other domains beyond the oil industry. The experimental results indicate that our multimodal approach significantly improves long-term prediction accuracy.-
Descrição: dc.descriptionShell Brasil-
Descrição: dc.descriptionArtificial Intelligence Lab. (Recod.ai) Institute of Computing Universidade Estadual de Campinas - UNICAMP, SP-
Descrição: dc.descriptionInstitute of Science and Technology São Paulo State University - UNESP, SP-
Descrição: dc.descriptionCenter for Petroleum Studies (CEPETRO) Universidade Estadual de Campinas - UNICAMP, SP-
Descrição: dc.descriptionDepartment of Computer Science Federal University of Lavras - UFLA, MG-
Descrição: dc.descriptionSchool of Mechanical Engineering Universidade Estadual de Campinas - UNICAMP, SP-
Descrição: dc.descriptionInstitute of Science and Technology São Paulo State University - UNESP, SP-
Formato: dc.format21581-21605-
Idioma: dc.languageen-
Relação: dc.relationNeural Computing and Applications-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectForecasting-
Palavras-chave: dc.subjectMixed-frequency time series-
Palavras-chave: dc.subjectMultimodal learning-
Palavras-chave: dc.subjectPre-salt oil field-
Título: dc.titleA multi-modal approach for mixed-frequency time series forecasting-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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