Fetching the paper…
Reading the bibliography…
This work summarizes two ways to accomplish Time-Series (TS) tasks in today's Large Language Model (LLM) context: LLM-for-TS (model-centric) designs and trains a fundamental large model, or fine-tunes a pre-trained LLM for TS data; TS-for-LLM (data-centric) converts TS into a model-friendly representation to enable the pre-trained LLM to handle TS data.
A review of deep learning methods for irregularly sampled medical time series data
Chenxi Sun, Shenda Hong, and et al · 2010
Earlier work this paper cites.
Computer science theory for the information age
John Hopcroft and Ravindran Kannan · 2013
Earlier work this paper cites.
Multivariate time series classification with WEASEL+MUSE
Patrick Schäfer and Ulf Leser · 2017
Earlier work this paper cites.
Forecasting at scale
Taylor SJ and Letham B · 2017
Earlier work this paper cites.
The UEA multivariate time series classification archive, 2018
Anthony J. Bagnall, Hoang Anh Dau, Jason Lines, Michael Flynn, James Large, Aaron Bostrom, Paul Southam, and Eamonn J. Keogh · 2018
Earlier work this paper cites.
The uea multivariate time series classification archive, 2018, 2018
Aaron Bostrom, Anthony Bagnall, Eamonn Keogh, Hoang Anh Dau, James Large, Jason Lines, Michael Flynn, and Paul Southam · 2018
Earlier work this paper cites.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Aäron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Earlier work this paper cites.
The ucr time series archive
Hoang Anh Dau, Anthony Bagnall, Kaveh Kamgar, Chin-Chia Michael Yeh, Yan Zhu, Shaghayegh Gharghabi, Chotirat Ann Ratanamahatana, and Eamonn Keogh · 2019
Earlier work this paper cites.
Multivariate lstm-fcns for time series classification
Fazle Karim, Somshubra Majumdar, Houshang Darabi, and Samuel Harford · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
Earlier work this paper cites.
Time-series generative adversarial networks
Jinsung Yoon, Daniel Jarrett, and Mihaela van der Schaar · 2019
Earlier work this paper cites.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton · 2020
Earlier work this paper cites.
Bootstrap your own latent - A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko · 2020
Earlier work this paper cites.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross B. Girshick · 2020
Earlier work this paper cites.
Reformer: The efficient transformer
Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya · 2020
Earlier work this paper cites.
N-BEATS: neural basis expansion analysis for interpretable time series forecasting
Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio · 2020
Earlier work this paper cites.
Clustering based contrastive learning for improving face representations
Vivek Sharma, Makarand Tapaswi, M. Saquib Sarfraz, and Rainer Stiefelhagen · 2020
Earlier work this paper cites.
Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Tongzhou Wang and Phillip Isola · 2020
Earlier work this paper cites.
Tapnet: Multivariate time series classification with attentional prototypical network
Xuchao Zhang, Yifeng Gao, Jessica Lin, and Chang-Tien Lu · 2020
Earlier work this paper cites.
Minirocket: A very fast (almost) deterministic transform for time series classification
Angus Dempster, Daniel F. Schmidt, and Geoffrey I. Webb · 2021
Earlier work this paper cites.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
Cited alongside, same era.
Time series extrinsic regression
Chang Wei Tan, Christoph Bergmeir, Francois Petitjean, and Geoffrey I Webb · 2021
Cited alongside, same era.
Unsupervised representation learning for time series with temporal neighborhood coding
Sana Tonekaboni, Danny Eytan, and Anna Goldenberg · 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.
A transformer-based framework for multivariate time series representation learning
George Zerveas, Srideepika Jayaraman, Dhaval Patel, Anuradha Bhamidipaty, and Carsten Eickhoff · 2021
Cited alongside, same era.
Supporting clustering with contrastive learning
Data-juicer: A one-stop data processing system for large language models
Daoyuan Chen, Yilun Huang, and et al · 2023
Closest in time.
Text-to-ecg: 12-lead electrocardiogram synthesis conditioned on clinical text reports
Hyunseung Chung, Jiho Kim, Joon-Myoung Kwon, Ki-Hyun Jeon, Min Sung Lee, and Edward Choi · 2023
Closest in time.
Simmtm: A simple pre-training framework for masked time-series modeling
Jiaxiang Dong, Haixu Wu, Haoran Zhang, Li Zhang, Jianmin Wang, and Mingsheng Long · 2023
Closest in time.
Large language models are zero-shot time series forecasters
Nate Gruver, Marc Finzi, Shikai Qiu, and Andrew Gordon Wilson · 2023
Closest in time.
Research and applications of extracting computational phenotype from vital sign time series
Shenda Hong, Hongyan Li, Chenxi Sun, and Junyuan Shang · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Dejiao Zhang, Feng Nan, Xiaokai Wei, Shang-Wen Li, Henghui Zhu, Kathleen R. McKeown, Ramesh Nallapati, Andrew O. Arnold, and Bing Xiang · 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.
Glm: General language model pretraining with autoregressive blank infilling
Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, and Jie Tang · 2022
Cited alongside, same era.
A reinforcement learning-informed pattern mining framework for multivariate time series classification
Ge Gao, Qitong Gao, Xi Yang, Miroslav Pajic, and Min Chi · 2022
Cited alongside, same era.
Learning representation for clustering via prototype scattering and positive sampling
Zhizhong Huang, Jie Chen, Junping Zhang, and Hongming Shan · 2022
Cited alongside, same era.
SLIC: self-supervised learning with iterative clustering for human action videos
Salar Hosseini Khorasgani, Yuxuan Chen, and Florian Shkurti · 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.
Closest in time.
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 · 2023
Closest in time.
Large language models are few-shot health learners
Xin Liu, Daniel McDuff, Geza Kovacs, Isaac R. Galatzer-Levy, Jacob E. Sunshine, Jiening Zhan, Ming-Zher Poh, Shun Liao, Paolo Di Achille, and Shwetak N. Patel · 2023
Closest in time.
A survey on time-series pre-trained models
Qianli Ma, Zhen Liu, Zhenjing Zheng, Ziyang Huang, Siying Zhu, Zhongzhong Yu, and James T. Kwok · 2023
Closest in time.
Large language models as general pattern machines
Suvir Mirchandani, Fei Xia, Pete Florence, Brian Ichter, Danny Driess, Montserrat Gonzalez Arenas, Kanishka Rao, Dorsa Sadigh, and Andy Zeng · 2023
Closest in time.
A time series is worth 64 words: Long-term forecasting with transformers
Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam · 2023
Closest in time.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, and et al · 2023
Closest in time.
Large-scale multi-modal pre-trained models: A comprehensive survey
Xiao Wang, Guangyao Chen, Guangwu Qian, Pengcheng Gao, Xiao-Yong Wei, Yaowei Wang, Yonghong Tian, and Wen Gao · 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.
Are transformers effective for time series forecasting?
Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu · 2023
Closest in time.
A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, and Ji-Rong Wen · 2023
Closest in time.
Simts: Rethinking contrastive representation learning for time series forecasting
Xiaochen Zheng, Xingyu Chen, Manuel Schürch, Amina Mollaysa, Ahmed Allam, and Michael Krauthammer · 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.
SVP-T: A shape-level variable-position transformer for multivariate time series classification
Rundong Zuo, Guozhong Li, Byron Choi, Sourav S. Bhowmick, Daphne Ngar-yin Mah, and Grace Lai-Hung Wong · 2023
Closest in time.
Frozen language model helps ecg zero-shot learning
Jun Li, Che Liu, Sibo Cheng, Rossella Arcucci, and Shenda Hong · 2024
Closest in time.