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The imputation of missing values in time series has many applications in healthcare and finance.
Scoring rules for continuous probability distributions
James E Matheson and Robert L Winkler · 1976
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Missing data: A comparison of neural network and expectation maximization techniques
Fulufhelo V Nelwamondo, Shakir Mohamed, and Tshilidzi Marwala · 2007
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Nearest neighbor imputation of species-level, plot-scale forest structure attributes from lidar data
Andrew T Hudak, Nicholas L Crookston, Jeffrey S Evans, David E Hall, and Michael J Falkowski · 2008
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Multi-task gaussian process prediction
Edwin V Bonilla, Kian Ming A Chai, and Christopher KI Williams · 2008
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MICE: Multivariate imputation by chained equations in r
S van Buuren and Karin Groothuis-Oudshoorn · 2010
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Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012
Ikaro Silva, George Moody, Daniel J Scott, Leo A Celi, and Roger G Mark · 2012
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A tensor-based method for missing traffic data completion
Huachun Tan, Guangdong Feng, Jianshuai Feng, Wuhong Wang, Yu-Jin Zhang, and Feng Li · 2013
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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ST-MVL: filling missing values in geo-sensory time series data
Xiuwen Yi, Yu Zheng, Junbo Zhang, and Tianrui Li · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 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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Recurrent neural networks for multivariate time series with missing values
Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu · 2018
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Multivariate time series imputation with generative adversarial networks
Yonghong Luo, Xiangrui Cai, Ying Zhang, Jun Xu, and Xiaojie Yuan · 2018
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Estimating missing data in temporal data streams using multi-directional recurrent neural networks
Jinsung Yoon, William R Zame, and Mihaela van der Schaar · 2018
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Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu acceleration
Jacob R Gardner, Geoff Pleiss, David Bindel, Kilian Q Weinberger, and Andrew Gordon Wilson · 2018
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Modeling long-and short-term temporal patterns with deep neural networks
Guokun Lai, Wei-Cheng Chang, Yiming Yang, and Hanxiao Liu · 2018
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E2GAN: End-to-end generative adversarial network for multivariate time series imputation
Yonghong Luo, Ying Zhang, Xiangrui Cai, and Xiaojie Yuan · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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High-dimensional multivariate forecasting with low-rank gaussian copula processes
David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, and Jan Gasthaus · 2019
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Latent ordinary differential equations for irregularly-sampled time series
GluonTS: Probabilistic and Neural Time Series Modeling in Python
Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, and Yuyang Wang · 2020
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Score-based generative modeling through stochastic differential equations
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Yulia Rubanova, Ricky TQ Chen, and David Duvenaud · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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GP-VAE: Deep probabilistic time series imputation
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Denoising diffusion probabilistic models
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Solving linear inverse problems using the prior implicit in a denoiser
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GLIMA: Global and local time series imputation with multi-directional attention learning
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Permutation invariant graph generation via score-based generative modeling
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Generative semi-supervised learning for multivariate time series imputation
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RDIS: Random drop imputation with self-training for incomplete time series data
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Multi-time attention networks for irregularly sampled time series
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Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting
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Uncertainty-aware variational-recurrent imputation network for clinical time series
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Multi-variate probabilistic time series forecasting via conditioned normalizing flows
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