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      Multivariate time series imputation with transformers

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      Author(s)
      Yıldız, A. Yarkın
      Koç, Emirhan
      Koç, Aykut
      Date
      2022-11-25
      Source Title
      IEEE Signal Processing Letters
      Print ISSN
      1070-9908
      Electronic ISSN
      1558-2361
      Publisher
      IEEE
      Volume
      29
      Pages
      2517 - 2521
      Language
      English
      Type
      Article
      Item Usage Stats
      42
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      56
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      Abstract
      Processing time series with missing segments is a fundamental challenge that puts obstacles to advanced analysis in various disciplines such as engineering, medicine, and economics. One of the remedies is imputation to fill the missing values based on observed values properly without undermining performance. We propose the Multivariate Time-Series Imputation with Transformers (MTSIT), a novel method that uses transformer architecture in an unsupervised manner for missing value imputation. Unlike the existing transformer architectures, this model only uses the encoder part of the transformer due to computational benefits. Crucially, MTSIT trains the autoencoder by jointly reconstructing and imputing stochastically-masked inputs via an objective designed for multivariate time-series data. The trained autoencoder is then evaluated for imputing both simulated and real missing values. Experiments show that MTSIT outperforms state-of-the-art imputation methods over benchmark datasets.
      Keywords
      Transformers
      Time series analysis
      Training
      Decoding
      Data models
      Medical services
      Computational modeling
      Permalink
      http://hdl.handle.net/11693/111208
      Published Version (Please cite this version)
      https://www.doi.org/10.1109/LSP.2022.3224880
      Collections
      • Department of Electrical and Electronics Engineering 4011
      • National Magnetic Resonance Research Center (UMRAM) 301
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