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Recent work has shown that simple linear models can outperform several Transformer based approaches in long term time-series forecasting.
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Tilmann Gneiting · 2011
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George EP Box, Gwilym M Jenkins, Gregory C Reinsel, and Greta M Ljung · 2015
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Tensorflow: learning functions at scale
Martín Abadi · 2016
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Learning linear dynamical systems via spectral filtering
Elad Hazan, Karan Singh, and Cyril Zhang · 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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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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Rnns incrementally evolving on an equilibrium manifold: A panacea for vanishing and exploding gradients?
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The surprising efficiency of framing geo-spatial time series forecasting as a video prediction task–insights from the iarai traffic4cast competition at neurips 2019
David P Kreil, Michael K Kopp, David Jonietz, Moritz Neun, Aleksandra Gruca, Pedro Herruzo, Henry Martin, Ali Soleymani, and Sepp Hochreiter · 2020
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Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting
Shizhan Liu, Hang Yu, Cong Liao, Jianguo Li, Weiyao Lin, Alex X Liu, and Schahram Dustdar · 2021
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Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long · 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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Flashattention: Fast and memory-efficient exact attention with io-awareness
Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christopher Ré · 2022
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Diagonal state spaces are as effective as structured state spaces
Ankit Gupta, Albert Gu, and Jonathan Berant · 2022
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The m5 accuracy competition: Results, findings and conclusions
S Makridakis, E Spiliotis, and V Assimakopoulos · 2020
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T Konstantin Rusch and Siddhartha Mishra · 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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Linformer: Self-attention with linear complexity
Sinong Wang, Belinda Z Li, Madian Khabsa, Han Fang, and Hao Ma · 2020
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On the benefits of maximum likelihood estimation for regression and forecasting
Pranjal Awasthi, Abhimanyu Das, Rajat Sen, and Ananda Theertha Suresh · 2021
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Reversible instance normalization for accurate time-series forecasting against distribution shift
Taesung Kim, Jinhee Kim, Yunwon Tae, Cheonbok Park, Jang-Ho Choi, and Jaegul Choo · 2021
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Efficiently modeling long sequences with structured state spaces
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Mantas Lukoševičius and Arnas Uselis · 2022
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M5 accuracy competition: Results, findings, and conclusions
Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos · 2022
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A time series is worth 64 words: Long-term forecasting with transformers
Yuqi Nie, Nam H Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam · 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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NHITS: Neural Hierarchical Interpolation for Time Series forecasting
Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler, and Artur Dubrawski · 2023
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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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