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dc.contributor.authorBlazquez-Garcia, Ane
dc.contributor.authorWickstrøm, Kristoffer Knutsen
dc.contributor.authorYu, Shujian
dc.contributor.authorMikalsen, Karl Øyvind
dc.contributor.authorBoubekki, Ahcene
dc.contributor.authorConde, Angel
dc.contributor.authorMori, Usue
dc.contributor.authorJenssen, Robert
dc.contributor.authorLozano, Jose A.
dc.date.accessioned2023-10-18T13:11:39Z
dc.date.available2023-10-18T13:11:39Z
dc.date.issued2023-01-31
dc.description.abstractMultivariate time series often contain missing values for reasons such as failures in data collection mechanisms. Since these missing values can complicate the analysis of time series data, imputation techniques are typically used to deal with this issue. However, the quality of the imputation directly affects the performance of downstream tasks. In this paper, we propose a selective imputation method that identifies a subset of timesteps with missing values to impute in a multivariate time series dataset. This selection, which will result in shorter and simpler time series, is based on both reducing the uncertainty of the imputations and representing the original time series as good as possible. In particular, the method uses multi-objective optimization techniques to select the optimal set of points, and in this selection process, we leverage the beneficial properties of the Multi-task Gaussian Process (MGP). The method is applied to different datasets to analyze the quality of the imputations and the performance obtained in downstream tasks, such as classification or anomaly detection. The results show that much shorter and simpler time series are able to maintain or even improve both the quality of the imputations and the performance of the downstream tasks.en_US
dc.identifier.citationBlazquez-Garcia, Wickstrøm, Yu, Mikalsen, Boubekki, Conde, Mori, Jenssen, Lozano. Selective Imputation for Multivariate Time Series Datasets with Missing Values. IEEE Transactions on Knowledge and Data Engineering. 2023;35(9):9490-9501en_US
dc.identifier.cristinIDFRIDAID 2182126
dc.identifier.doi10.1109/TKDE.2023.3240858
dc.identifier.issn1041-4347
dc.identifier.issn1558-2191
dc.identifier.urihttps://hdl.handle.net/10037/31585
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.journalIEEE Transactions on Knowledge and Data Engineering
dc.relation.projectIDNorges forskningsråd: 303514en_US
dc.relation.projectIDNorges forskningsråd: 309439en_US
dc.relation.projectIDNorges forskningsråd: 315029en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.titleSelective Imputation for Multivariate Time Series Datasets with Missing Valuesen_US
dc.type.versionsubmittedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US


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