dc.creator | Troncoso García, Ángela del Robledo | es |
dc.creator | Martínez Ballesteros, María del Mar | es |
dc.creator | Martínez Álvarez, Francisco | es |
dc.creator | Troncoso Lora, Alicia | es |
dc.date.accessioned | 2023-05-16T07:34:45Z | |
dc.date.available | 2023-05-16T07:34:45Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Troncoso García, Á.d.R., Martínez Ballesteros, M.d.M., Martínez Álvarez, F. y Troncoso Lora, A. (2023). A new approach based on association rules to add explainability to time series forecasting models. Information Fusion, 94, 169-180. https://doi.org/10.1016/j.inffus.2023.01.021. | |
dc.identifier.issn | 1566-2535 (impreso) | es |
dc.identifier.issn | 1872-6305 (online) | es |
dc.identifier.uri | https://hdl.handle.net/11441/146052 | |
dc.description.abstract | Machine learning and deep learning have become the most useful and powerful tools in the last years to mine information from large datasets. Despite the successful application to many research fields, it is widely known that some of these solutions based on artificial intelligence are considered black-box models, meaning that most experts find difficult to explain and interpret the models and why they generate such outputs. In this context, explainable artificial intelligence is emerging with the aim of providing black-box models with sufficient interpretability. Thus, models could be easily understood and further applied. This work proposes a novel method to explain black-box models, by using numeric association rules to explain and interpret multi-step time series forecasting models. Thus, a multi-objective algorithm is used to discover quantitative association rules from the target model. Then, visual explanation techniques are applied to make the rules more interpretable. Data from Spanish electricity energy consumption has been used to assess the suitability of the proposal. | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación PID2020-117954RB-C21 | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación TED2021-131311B-C22 | es |
dc.description.sponsorship | Junta de Andalucía PY20-00870 | es |
dc.description.sponsorship | Junta de Andalucía UPO-138516 | es |
dc.format | application/pdf | es |
dc.format.extent | 12 | es |
dc.language.iso | eng | es |
dc.publisher | ScienceDirect | es |
dc.relation.ispartof | Information Fusion, 94, 169-180. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Explainable AI | es |
dc.subject | Machine learning | es |
dc.subject | Time series forecasting | es |
dc.subject | Interpretability | es |
dc.subject | Association rules | es |
dc.title | A new approach based on association rules to add explainability to time series forecasting models | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | PID2020-117954RB-C21 | es |
dc.relation.projectID | TED2021-131311B-C22 | es |
dc.relation.projectID | PY20-00870 | es |
dc.relation.projectID | UPO-138516 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S1566253523000295?via%3Dihub | es |
dc.identifier.doi | 10.1016/j.inffus.2023.01.021 | es |
dc.journaltitle | Information Fusion | es |
dc.publication.volumen | 94 | es |
dc.publication.initialPage | 169 | es |
dc.publication.endPage | 180 | es |
dc.contributor.funder | Ministerio de Ciencia e Innovación (MICIN). España | es |
dc.contributor.funder | Junta de Andalucía | es |