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Optimizing online time-series data imputation through case-based reasoning
dc.contributor.author | Pascual Pañach, Josep |
dc.contributor.author | Sànchez-Marrè, Miquel |
dc.contributor.author | Cugueró Escofet, Miquel Àngel |
dc.contributor.other | Universitat Politècnica de Catalunya. Doctorat en Intel·ligència Artificial |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial |
dc.date.accessioned | 2022-11-10T09:13:25Z |
dc.date.available | 2022-11-10T09:13:25Z |
dc.date.issued | 2022 |
dc.identifier.citation | Pascual, J.; Sànchez-Marrè, M.; Cugueró-Escofet, M.À. Optimizing online time-series data imputation through case-based reasoning. A: International Conference of the Catalan Association for Artificial Intelligence. "Artificial intelligence research and development: proceedings of the 24th International Conference of the Catalan Association for Artificial Intelligence". Amsterdam: IOS Press, 2022, p. 87-90. ISBN 978-1-64368-327-0. DOI 10.3233/FAIA220320. |
dc.identifier.isbn | 978-1-64368-327-0 |
dc.identifier.uri | http://hdl.handle.net/2117/375989 |
dc.description.abstract | When working with Intelligent Decision Support Systems (IDSS), data quality could compromise decisions and therefore, an undesirable behaviour of the supported system. In this paper, a novel methodology for time-series online data imputation is proposed. A Case-Based Reasoning (CBR) system is used to provide such imputation approach. The CBR principle (i.e., solving the current problem using past solutions to similar problems) may be applied to data imputation, using values from similar past situations to replace incorrect or missing values. To improve the performance of the data imputation process, optimal case feature weights are obtained using genetic algorithms (GA). The proposed methodology is validated with data obtained from a real Waste Water Treatment Plant (WWTP) process. |
dc.format.extent | 4 p. |
dc.language.iso | eng |
dc.publisher | IOS Press |
dc.rights | Attribution-NonCommercial 4.0 International |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
dc.subject.lcsh | Decision support systems |
dc.subject.lcsh | Case-based reasoning |
dc.subject.lcsh | Machine learning |
dc.subject.other | Online data imputation |
dc.subject.other | Time-series |
dc.subject.other | Optimization |
dc.subject.other | Intelligent decision support |
dc.title | Optimizing online time-series data imputation through case-based reasoning |
dc.type | Conference report |
dc.subject.lemac | Sistemes d'ajuda a la decisió |
dc.subject.lemac | Raonament basat en casos |
dc.subject.lemac | Aprenentage automàtic |
dc.contributor.group | Universitat Politècnica de Catalunya. SIC - Sistemes Intel·ligents de Control |
dc.contributor.group | Universitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic |
dc.identifier.doi | 10.3233/FAIA220320 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://ebooks.iospress.nl/volumearticle/61226 |
dc.rights.access | Open Access |
local.identifier.drac | 34046274 |
dc.description.version | Postprint (published version) |
local.citation.author | Pascual, J.; Sànchez-Marrè, M.; Cugueró-Escofet, M.À. |
local.citation.contributor | International Conference of the Catalan Association for Artificial Intelligence |
local.citation.pubplace | Amsterdam |
local.citation.publicationName | Artificial intelligence research and development: proceedings of the 24th International Conference of the Catalan Association for Artificial Intelligence |
local.citation.startingPage | 87 |
local.citation.endingPage | 90 |