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In the burgeoning domain of Large Language Models (LLMs), there is a growing interest in applying LLM to time series forecasting, with multiple studies focused on leveraging textual prompts to further enhance the predictive prowess.
Swat: A water treatment testbed for research and training on ics security
Aditya P Mathur and Nils Ole Tippenhauer · 2016
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Ashish Vaswani and etc · 2017
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The uea multivariate time series classification archive, 2018
Anthony Bagnall and etc · 2018
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Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding
Kyle Hundman, Valentino Constantinou, Christopher Laporte, Ian Colwell, and Tom Soderstrom · 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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The m4 competition: Results, findings, conclusion and way forward
Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos · 2018
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Unsupervised scalable representation learning for multivariate time series
Jean-Yves Franceschi, Aymeric Dieuleveut, and Martin Jaggi · 2019
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N-beats: Neural basis expansion analysis for interpretable time series forecasting
Boris N Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Earlier work this paper cites.
Robust anomaly detection for multivariate time series through stochastic recurrent neural network
Ya Su, Youjian Zhao, Chenhao Niu, Rong Liu, Wei Sun, and Dan Pei · 2019
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Robust anomaly detection for multivariate time series through stochastic recurrent neural network
Ya Su, Youjian Zhao, Chenhao Niu, Rong Liu, Wei Sun, and Dan Pei · 2019
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Beatgan: Anomalous rhythm detection using adversarially generated time series
Bin Zhou, Shenghua Liu, Bryan Hooi, Xueqi Cheng, and Jing Ye · 2019
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Language models are few-shot learners
Tom B. Brown and etc · 2020
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ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels
Angus Dempster, Franois Petitjean, and Geoffrey I Webb · 2020
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Timeseries anomaly detection using temporal hierarchical one-class network
Lifeng Shen, Zhuocong Li, and James Kwok · 2020
Earlier work this paper cites.
Practical approach to asynchronous multivariate time series anomaly detection and localization
Ahmed Abdulaal, Zhuanghua Liu, and Tomer Lancewicki · 2021
Earlier work this paper cites.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
Cited alongside, same era.
Multivariate time series anomaly detection and interpretation using hierarchical inter-metric and temporal embedding
Zhihan Li, Youjian Zhao, Jiaqi Han, Ya Su, Rui Jiao, Xidao Wen, and Dan Pei · 2021
Cited alongside, same era.
Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long · 2021
Cited alongside, same era.
Anomaly transformer: Time series anomaly detection with association discrepancy
Jiehui Xu, Haixu Wu, Jianmin Wang, and Mingsheng Long · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Llm4ts: Two-stage fine-tuning for time-series forecasting with pre-trained llms
Ching Chang, Wen-Chih Peng, and Tien-Fu Chen · 2023
Closest in time.
Azul Garza and Max Mergenthaler-Canseco · 2023
Closest in time.
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick · 2023
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OpenAI · 2023
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Test: Text prototype aligned embedding to activate llm’s ability for time series
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BEit: BERT pre-training of image transformers
Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei · 2022
Cited alongside, same era.
N-hits: Neural hierarchical interpolation for time series forecasting
Cristian Challu and etc · 2022
Cited alongside, same era.
TFAD: A decomposition time series anomaly detection architecture with time-freq analysis
Zhang Chaoli, Zhou Tian, Wen Qingsong, and Sun Liang · 2022
Cited alongside, same era.
LoRA: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2022
Cited alongside, same era.
Flowformer: A transformer architecture for optical flow
Zhaoyang Huang, Xiaoyu Shi, Chao Zhang, Qiang Wang, Ka Chun Cheung, Hongwei Qin, Jifeng Dai, and Hongsheng Li · 2022
Cited alongside, same era.
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 · 2022
Cited alongside, same era.
Non-stationary transformers: Exploring the stationarity in time series forecasting
Yong Liu, Haixu Wu, Jianmin Wang, and Mingsheng Long · 2022
Cited alongside, same era.
Chenxi Sun, Yaliang Li, Hongyan Li, and linda Qiao · 2023
Closest in time.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample · 2023
Closest in time.
Transformers in time series: A survey
Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, and Liang Sun · 2023
Closest in time.
Timesnet: Temporal 2d-variation modeling for general time series analysis
Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long · 2023
Closest in time.
Promptcast: A new prompt-based learning paradigm for time series forecasting
Hao Xue and Flora D. Salim · 2023
Closest in time.
Dcdetector: Dual attention contrastive representation learning for time series anomaly detection
Yiyuan Yang, Chaoli Zhang, Tian Zhou, Qingsong Wen, and Liang Sun · 2023
Closest in time.
Are transformers effective for time series forecasting?
Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu · 2023
Closest in time.
One Fits All: Power general time series analysis by pretrained lm
Tian Zhou, Peisong Niu, Xue Wang, Liang Sun, and Rong Jin · 2023
Closest in time.
Gpt4mts: Prompt-based large language model for multimodal time-series forecasting
Furong Jia, Kevin Wang, Yixiang Zheng, Defu Cao, and Yan Liu · 2024
Closest in time.
Gpt4mts: Prompt-based large language model for multimodal time-series forecasting
Furong Jia, Kevin Wang, Yixiang Zheng, Defu Cao, and Yan Liu · 2024
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Time-LLM: Time series forecasting by reprogramming large language models
Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y. Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, and Qingsong Wen · 2024
Closest in time.
Lag-llama: Towards foundation models for probabilistic time series forecasting, 2024
Kashif Rasul and etc · 2024
Closest in time.