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Time series imputation remains a significant challenge across many fields due to the potentially significant variability in the type of data being modelled.
Supervised learning from incomplete data via an em approach
Zoubin Ghahramani and Michael I. Jordan · 1993
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Missing value estimation methods for dna microarrays
Olga G. Troyanskaya, Michael N. Cantor, Gavin Sherlock, Patrick O. Brown, Trevor J. Hastie, Robert Tibshirani, David Botstein, and Russ B. Altman · 2001
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A study of k-nearest neighbour as an imputation method
Gustavo E. A. P. A. Batista and Maria Carolina Monard · 2002
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Methods for the estimation of missing values in time series
David S. Fung · 2006
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Missing data: A comparison of neural network and expectation maximization techniques
Fulufhelo Vincent Nelwamondo, Shakir Mohamed, and T. Marwala · 2007
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Time Series Analysis by State Space Methods: Second Edition
T.J. Durbin and S.J. Koopman · 2012
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Imputation of incomplete non- stationary seasonal time series data
Yodah Walter.O, John Kihoro, K.H.O Athiany, and W KibunjaH · 2013
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Nearest neighbor imputation algorithms: a critical evaluation
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Hypernetworks, 2016
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Jinsung Yoon, William R. Zame, and Mihaela van der Schaar · 2017
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Brits: Bidirectional recurrent imputation for time series
Wei Cao, Dong Wang, Jian Li, Hao Zhou, Lei Li, and Yitan Li · 2018
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Multivariate time series imputation with generative adversarial networks
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Hypertime: Implicit neural representation for time series, 2022
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Fourier features let networks learn high frequency functions in low dimensional domains
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