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Time series forecasting is an important and forefront task in many real-world applications.
Recurrent neural networks and robust time series prediction
Jerome T Connor, R Douglas Martin, and Les E Atlas · 1994
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The use of arima models for reliability forecasting and analysis
Siu Lau Ho and Min Xie · 1998
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ℓ 1 \ell_{1} trend filtering
Seung-Jean Kim, Kwangmoo Koh, Stephen Boyd, and Dimitry Gorinevsky · 2009
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Learning with noisy labels
Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari · 2013
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Learning in nonstationary environments: A survey
Gregory Ditzler, Manuel Roveri, Cesare Alippi, and Robi Polikar · 2015
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Robust loss functions under label noise for deep neural networks
Aritra Ghosh, Himanshu Kumar, and PS Sastry · 2017
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Structured inference networks for nonlinear state space models
Rahul Krishnan, Uri Shalit, and David Sontag · 2017
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Making deep neural networks robust to label noise: A loss correction approach
Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu · 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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An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
Shaojie Bai, J Zico Kolter, and Vladlen Koltun · 2018
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Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama · 2018
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MentorNet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei · 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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Learning under concept drift: A review
Jie Lu, Anjin Liu, Fan Dong, Feng Gu, Joao Gama, and Guangquan Zhang · 2018
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Generalized cross entropy loss for training deep neural networks with noisy labels
Zhilu Zhang and Mert Sabuncu · 2018
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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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L_dmi: A novel information-theoretic loss function for training deep nets robust to label noise
Yilun Xu, Peng Cao, Yuqing Kong, and Yizhou Wang · 2019
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Resilient neural forecasting systems
Michael Bohlke-Schneider, Shubham Kapoor, and Tim Januschowski · 2020
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Normalizing kalman filters for multivariate time series analysis
Emmanuel de Bézenac, Syama Sundar Rangapuram, Konstantinos Benidis, Michael Bohlke-Schneider, Richard Kurle, Lorenzo Stella, Hilaf Hasson, Patrick Gallinari, and Tim Januschowski · 2020
Outlier impact characterization for time series data
Jianbo Li, Lecheng Zheng, Yada Zhu, and Jingrui He · 2021
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Time-series forecasting with deep learning: a survey
Bryan Lim and Stefan Zohren · 2021
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Understanding instance-level label noise: Disparate impacts and treatments
Yang Liu · 2021
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Dynamic gaussian mixture based deep generative model for robust forecasting on sparse multivariate time series
Yinjun Wu, Jingchao Ni, Wei Cheng, Bo Zong, Dongjin Song, Zhengzhang Chen, Yanchi Liu, Xuchao Zhang, Haifeng Chen, and Susan B Davidson · 2021
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Informer: Beyond efficient transformer for long sequence time-series forecasting
Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang · 2021
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Self-adaptive forecasting for improved deep learning on non-stationary time-series
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DivideMix: Learning with noisy labels as semi-supervised learning
Junnan Li, Richard Socher, and Steven C.H. Hoi · 2020
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Peer loss functions: Learning from noisy labels without knowing noise rates
Yang Liu and Hongyi Guo · 2020
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Normalized loss functions for deep learning with noisy labels
Xingjun Ma, Hanxun Huang, Yisen Wang, Simone Romano, Sarah Erfani, and James Bailey · 2020
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Robust regression with covariate filtering: Heavy tails and adversarial contamination
Ankit Pensia, Varun Jog, and Po-Ling Loh · 2020
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DeepAR: Probabilistic forecasting with autoregressive recurrent networks
David Salinas, Valentin Flunkert, Jan Gasthaus, and Tim Januschowski · 2020
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A review on outlier/anomaly detection in time series data
Ane Blázquez-García, Angel Conde, Usue Mori, and Jose A Lozano · 2021
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Sercan O Arik, Nathanael C Yoder, and Tomas Pfister · 2022
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Trimmed maximum likelihood estimation for robust learning in generalized linear models
Weihao Kong, Rajat Sen, Pranjal Awasthi, and Abhimanyu Das · 2022
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Robust learning of deep time series anomaly detection models with contaminated training data
Wenkai Li, Cheng Feng, Ting Chen, and Jun Zhu · 2022
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Learning from noisy labels with deep neural networks: A survey
Hwanjun Song, Minseok Kim, Dongmin Park, Yooju Shin, and Jae-Gil Lee · 2022
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Robust time series analysis and applications: An industrial perspective
Qingsong Wen, Linxiao Yang, Tian Zhou, and Liang Sun · 2022
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DeepTIMe: Deep time-index meta-learning for non-stationary time-series forecasting
Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, and Steven Hoi · 2022
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Robust probabilistic time series forecasting
Taeho Yoon, Youngsuk Park, Ernest K. Ryu, and Yuyang Wang · 2022
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Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting
Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin · 2022
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Are transformers effective for time series forecasting?
Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu · 2023
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