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Synthetic time series are often used in practical applications to augment the historical time series dataset for better performance of machine learning algorithms, amplify the occurrence of rare events, and also create counterfactual scenarios described by the time series.
Using dynamic time warping to find patterns in time series
Donald J Berndt and James Clifford · 1994
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Numerical optimization, 2006
Nocedal Jorge and J Wright Stephen · 2006
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Posterior regularization for structured latent variable models
Kuzman Ganchev, Joao Graça, Jennifer Gillenwater, and Ben Taskar · 2010
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The (mis) behaviour of markets: a fractal view of risk, ruin and reward
Benoit B Mandelbrot and Richard L Hudson · 2010
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Characterization of financial time series
Martin Sewell · 2011
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Generating sequences with recurrent neural networks
Alex Graves · 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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The synthetic data vault
Neha Patki, Roy Wedge, and Kalyan Veeramachaneni · 2016
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C-rnn-gan: Continuous recurrent neural networks with adversarial training
Olof Mogren · 2016
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Professor forcing: A new algorithm for training recurrent networks
Alex M Lamb, Anirudh Goyal ALIAS PARTH GOYAL, Ying Zhang, Saizheng Zhang, Aaron C Courville, and Yoshua Bengio · 2016
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Wavenet: A generative model for raw audio
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu · 2016
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The flash crash: High-frequency trading in an electronic market
Andrei Kirilenko, Albert S Kyle, Mehrdad Samadi, and Tugkan Tuzun · 2017
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Objective-reinforced generative adversarial networks (organ) for sequence generation models
Gabriel Lima Guimaraes, Benjamin Sanchez-Lengeling, Carlos Outeiral, Pedro Luis Cunha Farias, and Alán Aspuru-Guzik · 2017
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Deep learning
Yoshua Bengio, Ian Goodfellow, and Aaron Courville · 2017
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Real-valued (medical) time series generation with recurrent conditional gans
Cristóbal Esteban, Stephanie L Hyland, and Gunnar Rätsch · 2017
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Data driven prediction models of energy use of appliances in a low-energy house
Luis M Candanedo, Véronique Feldheim, and Dominique Deramaix · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 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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Trades, quotes and prices: financial markets under the microscope
Jean-Philippe Bouchaud, Julius Bonart, Jonathan Donier, and Martin Gould · 2018
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A semantic loss function for deep learning with symbolic knowledge
Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang, and Guy Broeck · 2018
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Molgan: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf · 2018
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Advanced statistical computing
Roger D Peng · 2018
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Chris Donahue, Julian McAuley, and Miller Puckette · 2018
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Time-series generative adversarial networks
Jinsung Yoon, Daniel Jarrett, and Mihaela Van der Schaar · 2019
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Embedding decision diagrams into generative adversarial networks
Yexiang Xue and Willem-Jan van Hoeve · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Time-series generation by contrastive imitation
Daniel Jarrett, Ioana Bica, and Mihaela van der Schaar · 2021
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Knowledge-based regularization in generative modeling
Naoya Takeishi and Yoshinobu Kawahara · 2021
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Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning
Jike Wang, Chang-Yu Hsieh, Mingyang Wang, Xiaorui Wang, Zhenxing Wu, Dejun Jiang, Benben Liao, Xujun Zhang, Bo Yang, Qiaojun He, et al · 2021
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Dc3: A learning method for optimization with hard constraints
Priya Donti, David Rolnick, and J Zico Kolter · 2021
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Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting
Kashif Rasul, Calvin Seward, Ingmar Schuster, and Roland Vollgraf · 2021
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Denoising diffusion implicit models
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Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
Get real: Realism metrics for robust limit order book market simulations, 2019
Svitlana Vyetrenko, David Byrd, Nick Petosa, Mahmoud Mahfouz, Danial Dervovic, Manuela Veloso, and Tucker Hybinette Balch · 2019
Cited alongside, same era.
Quantifying the carbon emissions of machine learning
Alexandre Lacoste, Alexandra Luccioni, Victor Schmidt, and Thomas Dandres · 2019
Cited alongside, same era.
Deep latent state space models for time-series generation
Linqi Zhou, Michael Poli, Winnie Xu, Stefano Massaroli, and Stefano Ermon · 2019
Cited alongside, same era.
Efficient generation of structured objects with constrained adversarial networks
Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Paolo Morettin, Stefano Teso, and Andrea Passerini · 2020
Cited alongside, same era.
Bootstrapping conditional gans for video game level generation
Ruben Rodriguez Torrado, Ahmed Khalifa, Michael Cerny Green, Niels Justesen, Sebastian Risi, and Julian Togelius · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Synthetic data in machine learning for medicine and healthcare
Richard J Chen, Ming Y Lu, Tiffany Y Chen, Drew FK Williamson, and Faisal Mahmood · 2021
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Towards generating real-world time series data
Hengzhi Pei, Kan Ren, Yuqing Yang, Chang Liu, Tao Qin, and Dongsheng Li · 2021
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Generative time series forecasting with diffusion, denoise, and disentanglement
Yan Li, Xinjiang Lu, Yaqing Wang, and Dejing Dou · 2022
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Gt-gan: General purpose time series synthesis with generative adversarial networks
Jinsung Jeon, Jeonghak Kim, Haryong Song, Seunghyeon Cho, and Noseong Park · 2022
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Generating multivariate time series with common source coordinated gan (cosci-gan)
Ali Seyfi, Jean-Francois Rajotte, and Raymond Ng · 2022
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How faithful is your synthetic data? sample-level metrics for evaluating and auditing generative models
Ahmed Alaa, Boris Van Breugel, Evgeny S Saveliev, and Mihaela van der Schaar · 2022
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Learning to simulate realistic limit order book markets from data as a world agent
Andrea Coletta, Aymeric Moulin, Svitlana Vyetrenko, and Tucker Balch · 2022
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Generative adversarial networks in time series: A systematic literature review
Eoin Brophy, Zhengwei Wang, Qi She, and Tomás Ward · 2023
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2023 stress test scenarios, 2023
Federal Reserve Board · 2023
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Synthetic market data and its applications
Jonathan Kinlay · 2023
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Synthetic data, real errors: how (not) to publish and use synthetic data
Boris van Breugel, Zhaozhi Qian, and Mihaela van der Schaar · 2023
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Beyond privacy: Navigating the opportunities and challenges of synthetic data
Boris van Breugel and Mihaela van der Schaar · 2023
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K-SHAP: Policy clustering algorithm for anonymous multi-agent state-action pairs
Andrea Coletta, Svitlana Vyetrenko, and Tucker Balch · 2023
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