End-to-end Multi-task Learning of Missing Value Imputation and Forecasting in Time-Series Data

Cited 4 time in webofscience Cited 0 time in scopus
  • Hit : 298
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorKim, Jinheeko
dc.contributor.authorKim, Taesungko
dc.contributor.authorChoi, Jang-Hoko
dc.contributor.authorChoo, Jaegulko
dc.date.accessioned2021-10-27T05:10:23Z-
dc.date.available2021-10-27T05:10:23Z-
dc.date.created2021-10-27-
dc.date.issued2021-01-
dc.identifier.citation25th International Conference on Pattern Recognition (ICPR), pp.8849 - 8856-
dc.identifier.issn1051-4651-
dc.identifier.urihttp://hdl.handle.net/10203/288340-
dc.description.abstractMultivariate time-series prediction is a common task, but it often becomes challenging due to missing data caused by unreliable sensors and other issues. In fact, inaccurate imputation of missing values can degrade the downstream prediction performance, so it may be better not to rely on the estimated values of missing data. Furthermore, observed data may contain noise, so denoising them can be helpful for the main task at hand. In response, we propose a novel approach that can automatically utilize the optimal combination of the observed and the estimated values to generate not only complete, but also noise-reduced data by our own gating mechanism. We evaluate our model on incomplete real-world time-series datasets and achieved state-of-the-art performance. Moreover, we present in-depth studies using a carefully designed, synthetic multivariate time-series dataset to verify the effectiveness of the proposed model. The ablation studies and the experimental analysis of the proposed gating mechanism show that it works as an effective denoising and imputation method for time-series classification tasks.-
dc.languageEnglish-
dc.publisherIEEE COMPUTER SOC-
dc.titleEnd-to-end Multi-task Learning of Missing Value Imputation and Forecasting in Time-Series Data-
dc.typeConference-
dc.identifier.wosid000681331401046-
dc.identifier.scopusid2-s2.0-85110497768-
dc.type.rimsCONF-
dc.citation.beginningpage8849-
dc.citation.endingpage8856-
dc.citation.publicationname25th International Conference on Pattern Recognition (ICPR)-
dc.identifier.conferencecountryUS-
dc.identifier.conferencelocationELECTR NETWORK-
dc.identifier.doi10.1109/ICPR48806.2021.9412112-
dc.contributor.localauthorChoo, Jaegul-
dc.contributor.nonIdAuthorKim, Jinhee-
dc.contributor.nonIdAuthorKim, Taesung-
dc.contributor.nonIdAuthorChoi, Jang-Ho-
Appears in Collection
RIMS Conference Papers
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 4 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0