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Yong Liu

Ph.D. Student
Tsinghua University
liuyong21@mails.tsinghua.edu.cn


About Me

I am currently a PhD student (from fall, 2021) at the School of Software of Tsinghua University and a member of the THUML, advised by Prof. MingSheng Long.

My research interests cover Time Series Analysis and Deep Learning. I am currently working on foundation time series models, large time series models, and multi-modal time series models. In addition to pure research, I also dedicate myself to promoting research on valuable real-world applications. My research aims to contribute to the advancement of intelligent systems capable of handling massive and complicated temporal data across domains, including finance, healthcare, industry, and environment.

For more information, you may take a look at my Google Scholar and GitHub.

News

Publications & Preprints

  1. arXiv
    Timer-XL: Long-Context Transformers for Unified Time Series Forecasting
    Yong Liu*, Guo Qin*, Xiangdong Huang, Jianmin Wang, Mingsheng Long#
    arXiv preprint, 2024.
    arXiv

  2. ICML
    Timer: Generative Pre-trained Transformers Are Large Time Series Models
    Yong Liu*, Haoran Zhang*, Chenyu Li*, Xiangdong Huang, Jianmin Wang, Mingsheng Long#
    International Conference on Machine Learning, 2024.
    arXiv Code Poster Slides Dataset Accepted

  3. NeurIPS
    AutoTimes: Autoregressive Time Series Forecasters via Large Language Models
    Yong Liu*, Guo Qin*, Xiangdong Huang, Jianmin Wang, Mingsheng Long#
    Conference on Neural Information Processing Systems, 2024.
    arXiv Code Slides Accepted

  4. ICLR
    iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
    Yong Liu*, Tengge Hu*, Haoran Zhang*, Haixu Wu, Shiyu Wang, Lintao Ma, Mingsheng Long#
    International Conference on Learning Representations, 2024.
    arXiv Code Poster Slides Blog Spotlight

  5. NeurIPS
    TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables
    Yuxuan Wang*, Haixu Wu*, Jiaxiang Dong, Yong Liu, Yunzhong Qiu, Haoran Zhang, Jianmin Wang, Mingsheng Long#
    Conference on Neural Information Processing Systems, 2024.
    arXiv Accepted

  6. ICLR
    TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
    Haiwu Wu*, Tengge Hu*, Yong Liu*, Hang Zhou, Jianmin Wang, Mingsheng Long#
    International Conference on Learning Representations, 2023.
    arXiv Code Slides Blog Accepted

  7. arXiv
    Deep Time Series Models: A Comprehensive Survey and Benchmark
    Yuxuan Wang*, Haixu Wu*, Jiaxiang Dong, Yong Liu, Mingsheng Long, Jianmin Wang#
    arXiv preprint, 2024.
    arXiv

  8. NeurIPS
    Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors
    Yong Liu*, Chenyu Li*, Jianmin Wang, Mingsheng Long#
    Conference on Neural Information Processing Systems, 2023.
    arXiv Code Poster Slides Blog Accepted

  9. NeurIPS
    Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting
    Yong Liu*, Haiwu Wu*, Jianmin Wang, Mingsheng Long#
    Conference on Neural Information Processing Systems, 2022.
    arXiv Code Poster Slides Blog Accepted

  10. JMLR
    Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs
    Kaichao You*, Yong Liu*, Ziyang Zhang, Jianmin Wang, Michael I. Jordan, Mingsheng Long#
    Journal of Machine Learning Research, 2022.
    arXiv Code Blog Accepted

  11. ICML
    LogME: Practical Assessment of Pre-trained Models for Transfer Learning
    Kaichao You*, Yong Liu*, Jianmin Wang, Mingsheng Long#
    International Conference on Machine Learning, 2021.
    arXiv Code Blog Accepted

Selected Projects

Deep Models for Time Series

Algorithm Library

System and Applications

Invited Talks

Services

Academic Services

Teaching Experiences

Education

Honors & Awards


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