Time series causal relationships discovery through feature importance and ensemble models

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Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorCastro, Manuel-
Autor(es): dc.creatorMendes Júnior, Pedro Ribeiro-
Autor(es): dc.creatorSoriano-Vargas, Aurea-
Autor(es): dc.creatorde Oliveira Werneck, Rafael-
Autor(es): dc.creatorMoreira Gonçalves, Maiara-
Autor(es): dc.creatorLusquino Filho, Leopoldo-
Autor(es): dc.creatorMoura, Renato-
Autor(es): dc.creatorZampieri, Marcelo-
Autor(es): dc.creatorLinares, Oscar-
Autor(es): dc.creatorFerreira, Vitor-
Autor(es): dc.creatorFerreira, Alexandre-
Autor(es): dc.creatorDavólio, Alessandra-
Autor(es): dc.creatorSchiozer, Denis-
Autor(es): dc.creatorRocha, Anderson-
Data de aceite: dc.date.accessioned2025-08-21T18:47:23Z-
Data de disponibilização: dc.date.available2025-08-21T18:47:23Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1038/s41598-023-37929-w-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/308303-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/308303-
Descrição: dc.descriptionInferring causal relationships from observational data is a key challenge in understanding the interpretability of Machine Learning models. Given the ever-increasing amount of observational data available in many areas, Machine Learning algorithms used for forecasting have become more complex, leading to a less understandable path of how a decision is made by the model. To address this issue, we propose leveraging ensemble models, e.g., Random Forest, to assess which input features the trained model prioritizes when making a forecast and, in this way, establish causal relationships between the variables. The advantage of these algorithms lies in their ability to provide feature importance, which allows us to build the causal network. We present our methodology to estimate causality in time series from oil field production. As it is difficult to extract causal relations from a real field, we also included a synthetic oil production dataset and a weather dataset, which is also synthetic, to provide the ground truth. We aim to perform causal discovery, i.e., establish the existing connections between the variables in each dataset. Through an iterative process of improving the forecasting of a target’s value, we evaluate whether the forecasting improves by adding information from a new potential driver; if so, we state that the driver causally affects the target. On the oil field-related datasets, our causal analysis results agree with the interwell connections already confirmed by tracer information; whenever the tracer data are available, we used it as our ground truth. This consistency between both estimated and confirmed connections provides us the confidence about the effectiveness of our proposed methodology. To our knowledge, this is the first time causal analysis using solely production data is employed to discover interwell connections in an oil field dataset.-
Descrição: dc.descriptionShell Brasil-
Descrição: dc.descriptionArtificial Intelligence Lab. Recod.ai Institute of Computing University of Campinas (Unicamp), SP-
Descrição: dc.descriptionCenter for Petroleum Engineering (CEPETRO) University of Campinas (Unicamp), SP-
Descrição: dc.descriptionSchool of Mechanical Engineering (FEM) University of Campinas (Unicamp), SP-
Descrição: dc.descriptionGroup of Automation and Integrated Systems São Paulo State University (Unesp), SP-
Descrição: dc.descriptionGroup of Automation and Integrated Systems São Paulo State University (Unesp), SP-
Descrição: dc.descriptionShell Brasil: 21373-6-
Idioma: dc.languageen-
Relação: dc.relationScientific Reports-
???dc.source???: dc.sourceScopus-
Título: dc.titleTime series causal relationships discovery through feature importance and ensemble models-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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