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Online updating of time series forecasting models aims to address the concept drifting problem by efficiently updating forecasting models based on streaming data.
Distribution of residual autocorrelations in autoregressive-integrated moving average time series models
George EP Box and David A Pierce · 1970
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Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton · 1991
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Asymptotic calibration
Dean P Foster and Rakesh V Vohra · 1998
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Regret in the on-line decision problem
Dean P Foster and Rakesh Vohra · 1999
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Convergence of exponentiated gradient algorithms
Simon I Hill and Robert C Williamson · 2001
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Potential-based algorithms in on-line prediction and game theory
Nicolo Cesa-Bianchi and Gábor Lugosi · 2003
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Online convex programming and generalized infinitesimal gradient ascent
Martin Zinkevich · 2003
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The problem of concept drift: definitions and related work
Alexey Tsymbal · 2004
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Internal regret in on-line portfolio selection
Gilles Stoltz and Gábor Lugosi · 2005
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Prediction, learning, and games
Nicolo Cesa-Bianchi and Gábor Lugosi · 2006
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From external to internal regret
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Reinforcement learning in feedback control: Challenges and benchmarks from technical process control
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Long short-term memory
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Online learning for time series prediction
Oren Anava, Elad Hazan, Shie Mannor, and Ohad Shamir · 2013
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio · 2014
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A survey on concept drift adaptation
João Gama, Indrė Žliobaitė, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia · 2014
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Online arima algorithms for time series prediction
Chenghao Liu, Steven CH Hoi, Peilin Zhao, and Jianling Sun · 2016
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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A dual-stage attention-based recurrent neural network for time series prediction
Yao Qin, Dongjin Song, Haifeng Chen, Wei Cheng, Guofei Jiang, and Garrison Cottrell · 2017
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 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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Test-time classifier adjustment module for model-agnostic domain generalization
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Time-series forecasting with deep learning: a survey
Bryan Lim and Stefan Zohren · 2021
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Gated transformer networks for multivariate time series classification
Minghao Liu, Shengqi Ren, Siyuan Ma, Jiahui Jiao, Yizhou Chen, Zhiguang Wang, and Wei Song · 2021
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Learning to learn the future: Modeling concept drifts in time series prediction
Xiaoyu You, Mi Zhang, Daizong Ding, Fuli Feng, and Yuanmin Huang · 2021
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Test-agnostic long-tailed recognition by test-time aggregating diverse experts with self-supervision
Yifan Zhang, Bryan Hooi, Lanqing Hong, and Jiashi Feng · 2021
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Shaojie Bai, J Zico Kolter, and Vladlen Koltun · 2018
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On tiny episodic memories in continual learning
Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet K Dokania, Philip HS Torr, and Marc’Aurelio Ranzato · 2019
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Off-policy deep reinforcement learning without exploration
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Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting
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An optimistic perspective on offline reinforcement learning
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Dark experience for general continual learning: a strong, simple baseline
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Exponentiated gradient meets gradient descent
Udaya Ghai, Elad Hazan, and Yoram Singer · 2020
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Informer: Beyond efficient transformer for long sequence time-series forecasting
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Rvs: What is essential for offline rl via supervised learning?
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Ddg-da: Data distribution generation for predictable concept drift adaptation
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Generalizing to evolving domains with latent structure-aware sequential autoencoder
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Transformers in time series: A survey
Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, and Liang Sun · 2022
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Tsmixer: An all-mlp architecture for time series forecasting
Si-An Chen, Chun-Liang Li, Nate Yoder, Sercan O Arik, and Tomas Pfister · 2023
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A comprehensive survey on test-time adaptation under distribution shifts
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A time series is worth 64 words: Long-term forecasting with transformers
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Learning fast and slow for online time series forecasting
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A survey on offline reinforcement learning: Taxonomy, review, and open problems
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Are transformers effective for time series forecasting?
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