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Recent work in synthetic data generation in the time-series domain has focused on the use of Generative Adversarial Networks.
Forecasting seasonals and trends by exponentially weighted moving averages
Charles C Holt · 1957
Earlier work this paper cites.
Forecasting sales by exponentially weighted moving averages
Peter R Winters · 1960
Earlier work this paper cites.
A general model for finite-sample effects in training and testing of competing classifiers
Sergey V Beiden, Marcus A Maloof, and Robert F Wagner · 2003
Earlier work this paper cites.
Near-uniform sampling of combinatorial spaces using xor constraints
Carla P Gomes, Ashish Sabharwal, and Bart Selman · 2006
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Cited alongside, same era.
Variational recurrent auto-encoders
Otto Fabius and Joost R Van Amersfoort · 2014
Cited alongside, same era.
C-rnn-gan: Continuous recurrent neural networks with adversarial training
Olof Mogren · 2016
Cited alongside, same era.
Real-valued (medical) time series generation with recurrent conditional gans
Cristóbal Esteban, Stephanie L Hyland, and Gunnar Rätsch · 2017
Cited alongside, same era.
Veegan: Reducing mode collapse in gans using implicit variational learning
Akash Srivastava, Lazar Valkov, Chris Russell, Michael U Gutmann, and Charles Sutton · 2017
Later among the works it cites.
N-beats: Neural basis expansion analysis for interpretable time series forecasting
Boris N Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio · 2019
Later among the works it cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Later among the works it cites.
Time-series generative adversarial networks
Jinsung Yoon, Daniel Jarrett, and Mihaela van der Schaar · 2019
Later among the works it cites.
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