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Time series imputation is a fundamental task for understanding time series with missing data.
On the estimation of arima models with missing values
Craig F Ansley and Robert Kohn · 1984
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A learning algorithm for continually running fully recurrent neural networks
Ronald J Williams and David Zipser · 1989
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The elements of statistical learning , volume 1
Jerome Friedman, Trevor Hastie, and Robert Tibshirani · 2001
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The treatment of missing values and its effect on classifier accuracy
Edgar Acuna and Caroline Rodriguez · 2004
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Differential equations driven by rough paths
Terry J Lyons, Michael Caruana, and Thierry Lévy · 2007
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Multiple imputation by chained equations: what is it and how does it work?
Melissa J Azur, Elizabeth A Stuart, Constantine Frangakis, and Philip J Leaf · 2011
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D Manning · 2015
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Improving multi-step prediction of learned time series models
Arun Venkatraman, Martial Hebert, and J Bagnell · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Directly modeling missing data in sequences with rnns: Improved classification of clinical time series
Zachary C Lipton, David Kale, and Randall Wetzel · 2016
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Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Tim Salimans and Durk P Kingma · 2016
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
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Cautionary tales on air-quality improvement in beijing
Shuyi Zhang, Bin Guo, Anlan Dong, Jing He, Ziping Xu, and Song Xi Chen · 2017
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Lipschitz continuity in model-based reinforcement learning
Kavosh Asadi, Dipendra Misra, and Michael Littman · 2018
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Estimation of the limit of detection in semiconductor gas sensors through linearized calibration models
Javier Burgués, Juan Manuel Jiménez-Soto, and Santiago Marco · 2018
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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
Gru-ode-bayes: Continuous modeling of sporadically-observed time series
Edward De Brouwer, Jaak Simm, Adam Arany, and Yves Moreau · 2019
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Set transformer: A framework for attention-based permutation-invariant neural networks
Juho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek, Seungjin Choi, and Yee Whye Teh · 2019
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Naomi: Non-autoregressive multiresolution sequence imputation
Yukai Liu, Rose Yu, Stephan Zheng, Eric Zhan, and Yisong Yue · 2019
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Cdsa: Cross-dimensional self-attention for multivariate, geo-tagged time series imputation
Jiawei Ma, Zheng Shou, Alireza Zareian, Hassan Mansour, Anthony Vetro, and Shih-Fu Chang · 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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Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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Maskgan: Better text generation via filling in the_
William Fedus, Ian Goodfellow, and Andrew M Dai · 2018
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Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
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Attentive neural processes
Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, Ali Eslami, Dan Rosenbaum, Oriol Vinyals, and Yee Whye Teh · 2018
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Generating wikipedia by summarizing long sequences
Peter J Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, and Noam Shazeer · 2018
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Scalable and accurate deep learning with electronic health records
Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M Dai, Nissan Hajaj, Michaela Hardt, Peter J Liu, Xiaobing Liu, Jake Marcus, Mimi Sun, et al · 2018
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Latent odes for irregularly-sampled time series
Yulia Rubanova, Ricky TQ Chen, and David Duvenaud · 2019
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Self-attention with functional time representation learning
Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan · 2019
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Set functions for time series
Max Horn, Michael Moor, Christian Bock, Bastian Rieck, and Karsten Borgwardt · 2020
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Neural controlled differential equations for irregular time series
Patrick Kidger, James Morrill, James Foster, and Terry Lyons · 2020
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Learning long-term dependencies in irregularly-sampled time series
Mathias Lechner and Ramin Hasani · 2020
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Diverse and admissible trajectory forecasting through multimodal context understanding
Seong Hyeon Park, Gyubok Lee, Manoj Bhat, Jimin Seo, Minseok Kang, Jonathan Francis, Ashwin R Jadhav, Paul Pu Liang, and Louis-Philippe Morency · 2020
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Meta-neighborhoods
Siyuan Shan, Yang Li, and Junier B Oliva · 2020
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Rdis: Random drop imputation with self-training for incomplete time series data
Tae-Min Choi, Ji-Su Kang, and Jong-Hwan Kim · 2021
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