ub.xmlui.mirage2.page-structure.muninLogoub.xmlui.mirage2.page-structure.openResearchArchiveLogo
    • EnglishEnglish
    • norsknorsk
  • Velg spraaknorsk 
    • EnglishEnglish
    • norsknorsk
  • Administrasjon/UB
Vis innførsel 
  •   Hjem
  • Fakultet for naturvitenskap og teknologi
  • Institutt for fysikk og teknologi
  • Artikler, rapporter og annet (fysikk og teknologi)
  • Vis innførsel
  •   Hjem
  • Fakultet for naturvitenskap og teknologi
  • Institutt for fysikk og teknologi
  • Artikler, rapporter og annet (fysikk og teknologi)
  • Vis innførsel
JavaScript is disabled for your browser. Some features of this site may not work without it.

Selective Imputation for Multivariate Time Series Datasets with Missing Values

Permanent lenke
https://hdl.handle.net/10037/31585
DOI
https://doi.org/10.1109/TKDE.2023.3240858
Thumbnail
Åpne
article.pdf (632.9Kb)
Innsendt manusversjon (PDF)
Dato
2023-01-31
Type
Journal article
Tidsskriftartikkel

Forfatter
Blazquez-Garcia, Ane; Wickstrøm, Kristoffer Knutsen; Yu, Shujian; Mikalsen, Karl Øyvind; Boubekki, Ahcene; Conde, Angel; Mori, Usue; Jenssen, Robert; Lozano, Jose A.
Sammendrag
Multivariate 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.
Forlag
IEEE
Sitering
Blazquez-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):9490-9501
Metadata
Vis full innførsel
Samlinger
  • Artikler, rapporter og annet (fysikk og teknologi) [1057]
Copyright 2023 The Author(s)

Bla

Bla i hele MuninEnheter og samlingerForfatterlisteTittelDatoBla i denne samlingenForfatterlisteTittelDato
Logg inn

Statistikk

Antall visninger
UiT

Munin bygger på DSpace

UiT Norges Arktiske Universitet
Universitetsbiblioteket
uit.no/ub - munin@ub.uit.no

Tilgjengelighetserklæring