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Deep learning performs remarkably well on many time series analysis tasks recently.
STL: A seasonal-trend decomposition procedure based on loess
Robert B Cleveland, William S Cleveland, Jean E McRae, and Irma Terpenning · 1990
Earlier work this paper cites.
Wavelet methods for time series analysis
Donald B Percival and Andrew T Walden · 2000
Earlier work this paper cites.
Surrogate time series
Thomas Schreiber and Andreas Schmitz · 2000
Earlier work this paper cites.
Beatgan: Anomalous rhythm detection using adversarially generated time series
Bin Zhou, Shenghua Liu, Bryan Hooi, Xueqi Cheng, and Jing Ye · 2002
Earlier work this paper cites.
Gaussian Processes for Machine Learning
Carl Edward Rasmussen and Christopher KI Williams · 2005
Earlier work this paper cites.
Constrained surrogate time series with preservation of the mean and variance structure
C J Keylock · 2006
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Deep gaussian processes
Andreas Damianou and Neil D Lawrence · 2013
Earlier work this paper cites.
Gaussian processes for time-series modelling
Stephen Roberts, Michael Osborne, Mark Ebden, Steven Reece, Neale Gibson, and Suzanne Aigrain · 2013
Earlier work this paper cites.
A parsimonious mixture of gaussian trees model for oversampling in imbalanced and multimodal time-series classification
Hong Cao, Vincent YF Tan, and John ZF Pang · 2014
Earlier work this paper cites.
Data augmentation for deep neural network acoustic modeling
X Cui, V Goel, and B Kingsbury · 2015
Earlier work this paper cites.
Generic and scalable framework for automated time-series anomaly detection
Nikolay Laptev, Saeed Amizadeh, et al · 2015
Earlier work this paper cites.
Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation
Christoph Bergmeir, Rob J. Hyndman, and José M. Benítez · 2016
Earlier work this paper cites.
Multi-scale convolutional neural networks for time series classification
Zhicheng Cui, Wenlin Chen, et al · 2016
Earlier work this paper cites.
Data augmentation for time series classification using convolutional neural networks
Arthur Le Guennec, Simon Malinowski, and Romain Tavenard · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Data preprocessing and augmentation for multiple short time series forecasting with recurrent neural networks
Slawek Smyl and Karthik Kuber · 2016
Earlier work this paper cites.
Dataset augmentation in feature space
Terrance DeVries and Graham W. Taylor · 2017
Earlier work this paper cites.
Real-valued (medical) time series generation with recurrent conditional gans
Cristóbal Esteban, Stephanie L Hyland, and Gunnar Rätsch · 2017
Earlier work this paper cites.
Deep learning for time-series analysis
John Cristian Borges Gamboa · 2017
Earlier work this paper cites.
Learning to compose domain-specific transformations for data augmentation
Alexander J Ratner, Henry R Ehrenberg, Zeshan Hussain, Jared Dunnmon, and Christopher Ré · 2017
Earlier work this paper cites.
Doubly stochastic variational inference for deep gaussian processes
Hugh Salimbeni and Marc Peter Deisenroth · 2017
Cited alongside, same era.
Data augmentation of wearable sensor data for Parkinson’s disease monitoring using convolutional neural networks
Terry T Um, Franz M J Pfister, Daniel Pichler, Satoshi Endo, Muriel Lang, Sandra Hirche, Urban Fietzek, and Dana Kulić · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, et al · 2017
Cited alongside, same era.
Time series classification from scratch with deep neural networks: A strong baseline
Zhiguang Wang, Weizhong Yan, and Tim Oates · 2017
Cited alongside, same era.
Data augmentation using synthetic data for time series classification with deep residual networks
Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, and Pierre-Alain Muller · 2018
Cited alongside, same era.
Time series anomaly detection using convolutional neural networks and transfer learning
Tailai Wen and Roy Keyes · 2019
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RobustTrend: A Huber loss with a combined first and second order difference regularization for time series trend filtering
Qingsong Wen, Jingkun Gao, Xiaomin Song, Liang Sun, and Jian Tan · 2019
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RobustSTL: A robust seasonal-trend decomposition algorithm for long time series
Qingsong Wen, Jingkun Gao, Xiaomin Song, Liang Sun, Huan Xu, and Shenghuo Zhu · 2019
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Time-series generative adversarial networks
Jinsung Yoon, Daniel Jarrett, and Mihaela van der Schaar · 2019
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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, et al · 2020
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SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary
Alberto Fernández, Salvador Garcia, Francisco Herrera, and Nitesh V Chawla · 2018
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Cost-sensitive convolution based neural networks for imbalanced time-series classification
Yue Geng and Xinyu Luo · 2018
Cited alongside, same era.
Feature-based comparison and generation of time series
Lars Kegel, Martin Hahmann, and Wolfgang Lehner · 2018
Cited alongside, same era.
DOPING: Generative data augmentation for unsupervised anomaly detection with gan
Swee Kiat Lim, Yi Loo, Ngoc-Trung Tran, Ngai-Man Cheung, Gemma Roig, and Yuval Elovici · 2018
Cited alongside, same era.
Feature representation and data augmentation for human activity classification based on wearable IMU sensor data using a deep LSTM neural network
Odongo Steven Eyobu and Dong Seog Han · 2018
Cited alongside, same era.
AutoAugment: Learning augmentation strategies from data
Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V. Le · 2019
Cited alongside, same era.
Deep learning for time series classification: a review
Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, et al · 2019
Cited alongside, same era.
Kasun Bandara, Hansika Hewamalage, Yuan-Hao Liu, Yanfei Kang, and Christoph Bergmeir · 2020
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RandAugment: Practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
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Modeling continuous stochastic processes with dynamic normalizing flows
Ruizhi Deng, Bo Chang, Marcus A Brubaker, Greg Mori, and Andreas Lehrmann · 2020
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Data augmentation for time series: traditional vs generative models on capacitive proximity time series
Biying Fu, Florian Kirchbuchner, and Arjan Kuijper · 2020
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Robusttad: Robust time series anomaly detection via decomposition and convolutional neural networks
Jingkun Gao, Xiaomin Song, Qingsong Wen, Pichao Wang, Liang Sun, and Huan Xu · 2020
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Datsing: Data augmented time series forecasting with adversarial domain adaptation
Hailin Hu, MingJian Tang, and Chengcheng Bai · 2020
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An empirical survey of data augmentation for time series classification with neural networks
Brian Kenji Iwana and Seiichi Uchida · 2020
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GRATIS: Generating time series with diverse and controllable characteristics
Yanfei Kang, Rob J Hyndman, and Feng Li · 2020
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Normalizing flows: An introduction and review of current methods
Ivan Kobyzev, Simon Prince, and Marcus Brubaker · 2020
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Stock market forecasting with super-high dimensional time-series data using convlstm, trend sampling, and specialized data augmentation
Si Woon Lee and Ha Young Kim · 2020
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Fast RobustSTL: Efficient and robust seasonal-trend decomposition for time series with complex patterns
Qingsong Wen, Zhe Zhang, Yan Li, and Liang Sun · 2020
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Adversarial autoaugment
Xinyu Zhang, Qiang Wang, Jian Zhang, and Zhao Zhong · 2020
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MODALS: Modality-agnostic automated data augmentation in the latent space
Tsz-Him Cheung and Dit-Yan Yeung · 2021
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Adaptive weighting scheme for automatic time-series data augmentation
Elizabeth Fons, Paula Dawson, Xiao-jun Zeng, John Keane, and Alexandros Iosifidis · 2021
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RobustPeriod: Time-frequency mining for robust multiple periodicities detection
Qingsong Wen, Kai He, Liang Sun, Yingying Zhang, Min Ke, and Huan Xu · 2021
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