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Pre-trained Large Language Models (LLMs) encapsulate large amounts of knowledge and take enormous amounts of compute to train.
The Econometric Analysis of Time Series
Andrew C. Harvey · 1990
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Attention is all you need
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A multi-horizon quantile recurrent forecaster, 2018
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Mqtransformer: Multi-horizon forecasts with context dependent and feedback-aware attention
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Heavy-tailed universality predicts trends in test accuracies for very large pre-trained deep neural networks
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
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The early phase of neural network training
Jonathan Frankle, David J Schwab, and Ari S Morcos · 2020
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On the opportunities and risks of foundation models
Rishi Bommasani et al · 2021
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Informer: Beyond efficient transformer for long sequence time-series forecasting, 2021
Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang · 2021
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Implicit self-regularization in deep neural networks: Evidence from random matrix theory and implications for learning
Charles H Martin and Michael W Mahoney · 2021
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Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data
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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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Yi-Lin Sung, Jaemin Cho, and Mohit Bansal · 2022
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Multimodal chain-of-thought reasoning in language models
Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, and Alex Smola · 2023
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Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 2023
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Are transformers effective for time series forecasting?
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Yaoqing Yang, Ryan Theisen, Liam Hodgkinson, Joseph E Gonzalez, Kannan Ramchandran, Charles H Martin, and Michael W Mahoney · 2022
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Transformers in time series: A survey
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Neural networks can learn representations with gradient descent
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Emergent abilities of large language models, 2022
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One fits all: Power general time series analysis by pretrained LM, 2023
Tian Zhou, PeiSong Niu, Xue Wang, Liang Sun, and Rong Jin · 2023
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Introducing MPT-7B: A new standard for open-source, commercially usable llms, 2023
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A time series is worth 64 words: Long-term forecasting with transformers, 2023
Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam · 2023
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Temporal data meets LLM – explainable financial time series forecasting, 2023
Xinli Yu, Zheng Chen, Yuan Ling, Shujing Dong, Zongyi Liu, and Yanbin Lu · 2023
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DINOv2: Learning robust visual features without supervision, 2023
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Chronos: Learning the Language of Time Series (Github)
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Improved weight matrix diagnostics for time series forecasting models
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Are language models actually useful for time series forecasting?, 2024
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