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Xiangfei Qiu

Phd Student
East China Normal University
xfqiu@stu.ecnu.edu.cn Email
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πŸ‘‹ About Me

I am currently a Phd student (from fall, 2023) at the School of Data Science and Engineering of East China Normal University and a member of the Decision Intelligence Lab, advised by Prof.Β Jilin Hu and Prof. Bin Yang. I also work closely with Prof. Christian S. Jensen .

πŸ” Research Topics

My research interests cover Time Series Analysis and Deep Learning. I am currently working on foundation time series models, and time series benchmarking. 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, industry, and environment. For more information, you may take a look at my Google Scholar Google Scholar Citations and GitHub GitHub Stars.

πŸ”₯ News

  • [May. 2026] Our survey paper "A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspective" has been accepted by IJCAI 2026.
  • [May. 2026] Our papers "DAG", "TFMixer", "SEER", and "TeamWork" have been accepted to ICML 2026.
  • [Apr. 2026] Our time series anomaly prediction algorithm TAP has been accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE).
  • [Apr. 2026] Time series application platform (EasyTime, ICDE25) was selected as most influential papers by Paper Digest (Rank 1st / 397 Accepted Papers).
  • [Jan. 2026] Our papers "GCGNet & ASTGI" have been accepted to ICLR 2026.
  • [Dec. 2025] I have been selected for the Youth Science and Technology Talents Cultivation Project -- Doctoral Student Special Program (China Association for Science and Technology).
  • [Dec. 2025] Time series model (DUET, KDD25) was selected as most influential papers by Paper Digest.
  • [Nov. 2025] Our paper "Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline" has been accepted for an oral presentation at AAAI 2026.
  • [Oct. 2025] Our paper "EDAD: An Encode-then-Decompose Approach to Unsupervised Time Series Anomaly Detection on Contaminated Training Data" has been accepted by ICDE 2026.
  • [Oct. 2025] I have been awarded the National Scholarship.
  • [Sep. 2025] Our papers "SRSNet" and "DBLoss" have been accepted to NeurIPS 2025, with one Spotlight and one Poster.
  • [Jun. 2025] I am honored to receive recognition as an Outstanding Reviewer for the SIGKDD 2025 Research Track and an Excellent Reviewer for the Applied Data Science (ADS) Track.
  • [May. 2025] I have been awarded the first place in Master Group of 2025 CCF Academic Show.
  • [May. 2025] My paper "TAB: Unified Benchmarking of Time Series Anomaly Detection Methods" has been accepted by PVLDB 2025.
  • [May. 2025] Our paper "TSFM-Bench: A Comprehensive and Unified Benchmark of Foundation Models for Time Series Forecasting" has been accepted by SIGKDD 2025.
  • [May. 2025] Our paper "SSD-TS: Exploring the potential of linear state space models for diffusion models in time series imputation" has been accepted by SIGKDD 2025.
  • [May. 2025] My paper "K2VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting" has been accepted as ICML 2025 Spotlight.
  • [Jan. 2025] Our paper "CATCH: Channel-Aware Multivariate Time Series Anomaly Detection via Frequency Patching" has been accepted by ICLR 2025.
  • [Dec. 2024] My paper "EasyTime: Time Series Forecasting Made Easy" has been accepted by ICDE 2025.
  • [Nov. 2024] My paper "DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting" has been accepted by SIGKDD 2025.
  • [Oct. 2024] I have been awarded the National Scholarship.
  • [Aug. 2024] My paper "TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods" receives VLDB 2024 Best Research Paper Award Nomination.
  • [Aug. 2024] We have created a leaderboard for time series analytics, called OpenTS.


πŸ“ Publications & Preprints

  1. TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods PVLDB
    TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
    Xiangfei Qiu, Jilin Hu#, Lekui Zhou, Xingjian Wu, Junyang Du, Buang Zhang, Chenjuan Guo, Aoying Zhou, Christian S. Jensen, Zhenli Sheng, Bin Yang
    International Conference on Very Large Databases, 2024.
    PDF Code BibTeX Blog πŸ† PVLDB, CCF A, Accepted (Best Paper Award Nomination)
  2. TAB: Unified Benchmarking of Time Series Anomaly Detection Methods PVLDB
    TAB: Unified Benchmarking of Time Series Anomaly Detection Methods
    Xiangfei Qiu, Zhe Li, Wanghui Qiu, Shiyan Hu, Lekui Zhou, Xingjian Wu, Zhengyu Li Chenjuan Guo, Aoying Zhou, Zhenli Sheng, Jilin Hu, Christian S. Jensen, Bin Yang
    International Conference on Very Large Databases, 2025.
    PDF Code PVLDB, CCF A, Accepted
  3. DBLoss: Decomposition-based Loss Function for Time Series Forecasting NeurIPS
    DBLoss: Decomposition-based Loss Function for Time Series Forecasting
    Xiangfei Qiu, Xingjian Wu, Hanyin Cheng, Xvyuan Liu, Chenjuan Guo, Jilin Hu#, Bin Yang
    Neural Information Processing Systems Conference, 2025.
    PDF Code Blog NeurIPS, CCF A, Accepted
  4. DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting SIGKDD
    DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting
    Xiangfei Qiu, Xingjian Wu, Yan Lin, Chenjuan Guo, Jilin Hu#, Bin Yang
    ACM Knowledge Discovery and Data Mining, 2025.
    PDF Code BibTeX Blog πŸ† SIGKDD, CCF A, Accepted (PaperDigest Most Influential Paper)
  5. DAG: A Dual Correlation Network for Time Series Forecasting with Exogenous Variables ICML
    DAG: A Dual Correlation Network for Time Series Forecasting with Exogenous Variables
    Xiangfei Qiu, Yuhan Zhu, Zhengyu Li, Hanyin Cheng, Xingjian Wu, Chenjuan Guo, Bin Yang, Jilin Hu#
    International Conference on Machine Learning, 2026.
    PDF Code ICML, CCF A, Accepted
  6. Bridging Time and Frequency: A Joint Modeling Framework for Irregular Multivariate Time Series Forecasting ICML
    Bridging Time and Frequency: A Joint Modeling Framework for Irregular Multivariate Time Series Forecasting
    Xiangfei Qiu, Kangjia Yan, Xvyuan Liu, Xingjian Wu, Jilin Hu#
    International Conference on Machine Learning, 2026.
    PDF ICML, CCF A, Accepted
  7. SEER: Transformer-based Robust Time Series Forecasting via Automated Patch Enhancement and Replacement ICML
    SEER: Transformer-based Robust Time Series Forecasting via Automated Patch Enhancement and Replacement
    Xiangfei Qiu, Xvyuan Liu, Tianen Shen, Xingjian Wu, Hanyin Cheng, Bin Yang, Jilin Hu#
    International Conference on Machine Learning, 2026.
    PDF ICML, CCF A, Accepted
  8. A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspective IJCAI
    A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspective
    Xiangfei Qiu, Hanyin Cheng, Xingjian Wu, Junkai Lu, Jilin Hu, Chenjuan Guo, Christian S. Jensen, Bin Yang
    International Joint Conference on Artificial Intelligence, 2026.
    PDF Code Blog IJCAI, Core A*, Accepted
  9. Hermes: A Multi-Scale Spatial-Temporal Hypergraph Network for Stock Time Series Forecasting arXiv
    Hermes: A Multi-Scale Spatial-Temporal Hypergraph Network for Stock Time Series Forecasting
    Xiangfei Qiu, Liu Yang, Xiangyu Xu, Hanyin Cheng, Xingjian Wu, Rongjia Wu, Zhigang Zhang, Ding Tu, Chenjuan Guo, Bin Yang, Christian S. Jensen, Jilin Hu#
    arXiv preprint, 2025.
    PDF
  10. EasyTime: Time Series Forecasting Made Easy ICDE
    EasyTime: Time Series Forecasting Made Easy
    Xiangfei Qiu*, Xiuwen Li*, Ruiyang Pang*, Zhicheng Pan*, Xingjian Wu*, Liu Yang*, Jilin Hu, Yang Shu, Xuesong Lu, Chengcheng Yang, Chenjuan Guo, Aoying Zhou, Christian S. Jensen and Bin Yang.
    IEEE International Conference on Data Engineering, 2025.
    PDF πŸ† ICDE, CCF A, Accepted (PaperDigest Most Influential Paper)
  11. ASTGI: Adaptive Spatio-Temporal Graph Interactions for Irregular Multivariate Time Series Forecasting ICLR
    ASTGI: Adaptive Spatio-Temporal Graph Interactions for Irregular Multivariate Time Series Forecasting
    Xvyuan Liu*, Xiangfei Qiu*, Hanyin Cheng, Xingjian Wu, Chenjuan Guo, Bin Yang, Jilin Hu
    International Conference on Learning Representations, 2026.
    PDF ICLR, CCF A, Accepted
  12. Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline AAAI
    Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline
    Xvyuan Liu*, Xiangfei Qiu*, Xingjian Wu*, Zhengyu Li, Chenjuan Guo, Jilin Hu, Bin Yang
    AAAI Conference on Artificial Intelligence, 2026.
    PDF Code Blog ✨ AAAI, CCF A, Accepted (Oral, Top 4%)
  13. K2VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting ICML
    K2VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting
    Xingjian Wu*, Xiangfei Qiu*, Hongfan Gao, Jilin Hu, Chenjuan Guo, Bin Yang#
    International Conference on Machine Learning, 2025.
    PDF Code Blog ✨ ICML, CCF A, Accepted (Spotlight, Top 2.6%)
  14. TSFM-Bench: A Comprehensive and Unified Benchmark of Foundation Models for Time Series Forecasting SIGKDD
    TSFM-Bench: A Comprehensive and Unified Benchmark of Foundation Models for Time Series Forecasting
    Zhe Li, Xiangfei Qiu, Peng Chen, Yihang Wang, Hanyin Cheng, Yang Shu, Jilin Hu, Chenjuan Guo, Aoying Zhou, Qingsong Wen, Christian S. Jensen, Bin Yang
    ACM Knowledge Discovery and Data Mining, 2025.
    PDF Code Blog SIGKDD, CCF A, Accepted
  15. GCGNet: Graph-Consistent Generative Network for Time Series Forecasting with Exogenous Variables ICLR
    GCGNet: Graph-Consistent Generative Network for Time Series Forecasting with Exogenous Variables
    Zhengyu Li, Xiangfei Qiu, Yuhan Zhu, Xingjian Wu, Jilin Hu, Chenjuan Guo, Bin Yang
    International Conference on Learning Representations, 2026.
    PDF ICLR, CCF A, Accepted
  16. Enhancing Time Series Forecasting through Selective Representation Spaces: A Patch Perspective NeurIPS
    Enhancing Time Series Forecasting through Selective Representation Spaces: A Patch Perspective
    Xingjian Wu, Xiangfei Qiu, Hanyin Cheng, Zhengyu Li, Jilin Hu, Chenjuan Guo, Bin Yang#
    PDF Code Blog ✨ NeurIPS, CCF A, Accepted (Spotlight, Top 3.2%)
  17. CATCH: Channel-Aware Multivariate Time Series Anomaly Detection via Frequency Patching ICLR
    CATCH: Channel-Aware Multivariate Time Series Anomaly Detection via Frequency Patching
    Xingjian Wu, Xiangfei Qiu, Zhengyu Li, Yihang Wang, Jilin Hu, Chenjuan Guo, Hui Xiong, Bin Yang#
    International Conference on Learning Representations, 2025.
    PDF Code Blog ICLR, CCF A, Accepted
  18. MEMTS: Internalizing Domain Knowledge via Parameterized Memory for Retrieval-Free Domain Adaptation of Time Series Foundation Models arXiv
    MEMTS: Internalizing Domain Knowledge via Parameterized Memory for Retrieval-Free Domain Adaptation of Time Series Foundation Models
    Xiaoyun Yu*, Li Fan*, Xiangfei Qiu, Nanqing Dong, Yonggui Huang, Honggang Qi, Geguang Pu, Wanli Ouyang, Xi Chen#, Jilin Hu#
    arXiv preprint, 2026.
    PDF
  19. An Encode-then-Decompose Approach to Unsupervised Time Series Anomaly Detection on Contaminated Training Data ICDE
    An Encode-then-Decompose Approach to Unsupervised Time Series Anomaly Detection on Contaminated Training Data
    Buang Zhang, Tung Kieu, Xiangfei Qiu, Chenjuan Guo, Jilin Hu, Aoying Zhou, Christian S. Jensen, Bin Yang
    IEEE International Conference on Data Engineering, 2026.
    PDF ICDE, CCF A, Accepted
  20. SSD-TS: Exploring the potential of linear state space models for diffusion models in time series imputation SIGKDD
    SSD-TS: Exploring the potential of linear state space models for diffusion models in time series imputation
    Hongfan Gao, Wangmeng Shen, Xiangfei Qiu, Ronghui Xu, Jilin Hu#, Bin Yang
    ACM Knowledge Discovery and Data Mining, 2025.
    PDF Code SIGKDD, CCF A, Accepted
  21. FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecasting arXiv
    FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecasting
    Xingjian Wu, Hanyin Cheng, Xiangfei Qiu, Zhengyu Li, Jilin Hu, Chenjuan Guo, Bin Yang#
    arXiv preprint, 2025.
    PDF
  22. LightGTS-Cov: Covariate-Enhanced Time Series Forecasting arXiv
    LightGTS-Cov: Covariate-Enhanced Time Series Forecasting
    Yong Shang, Zhipeng Yao, Ning Jin, Xiangfei Qiu, Hui Zhang, Bin Yang
    arXiv preprint, 2026.
    PDF
  23. TeamWork: Multivariate Time Series Anomaly Detection via Asymmetric Role-aware Channel Modeling ICML
    TeamWork: Multivariate Time Series Anomaly Detection via Asymmetric Role-aware Channel Modeling
    Shiyan Hu, Dingxue Zhang, Jianxin Jin, Xiangfei Qiu, Bin Yang, Chenjuan Guo
    International Conference on Machine Learning, 2026.
    ICML, CCF A, Accepted
  24. TAP: Time Series Anomaly Prediction via Adaptive Period Modeling and Dual Representation Learning TKDE
    TAP: Time Series Anomaly Prediction via Adaptive Period Modeling and Dual Representation Learning
    Shiyan Hu, Kai Zhao, Chenjuan Guo, Xiangfei Qiu, Yang Shu, Jilin Hu, Christian S. Jensen, Bin Yang
    IEEE Transactions on Knowledge and Data Engineering, 2026.
    PDF Blog TKDE, CCF A, Accepted
  25. TimeART: Towards Agentic Time Series Reasoning via Tool-Augmentation arXiv
    TimeART: Towards Agentic Time Series Reasoning via Tool-Augmentation
    Xingjian Wu, Junkai Lu, Zhengyu Li, Xiangfei Qiu, Jilin Hu, Chenjuan Guo, Christian S. Jensen, Bin Yang, Bin Yang#
    arXiv preprint, 2025.
    PDF
  26. Task-Aware Mixture-of-Experts for Time Series Analysis arXiv
    Task-Aware Mixture-of-Experts for Time Series Analysis
    Xingjian Wu, Zhengyu Li, Hanyin Cheng, Xiangfei Qiu, Jilin Hu, Chenjuan Guo, Bin Yang#
    arXiv preprint, 2025.
    PDF
  27. ST-EVO: Towards Generative Spatio-Temporal Evolution of Multi-Agent Communication Topologies arXiv
    ST-EVO: Towards Generative Spatio-Temporal Evolution of Multi-Agent Communication Topologies
    Xingjian Wu, Xvyuan Liu, Junkai Lu, Siyuan Wang, Xiangfei Qiu, Yang Shu, Jilin Hu, Chenjuan Guo, Bin Yang
    arXiv preprint, 2026.
    PDF
  28. AutoCTS++: zero-shot joint neural architecture and hyperparameter search for correlated time series forecasting VLDBJ
    AutoCTS++: zero-shot joint neural architecture and hyperparameter search for correlated time series forecasting
    Xinle Wu, Xingjian Wu, Bin Yang#, Lekui Zhou, Chenjuan Guo, Xiangfei Qiu, Jilin Hu, Zhenli Sheng, Christian S. Jensen
    VLDB Journal, 2024.
    PDF Code BibTeX Blog VLDBJ, CCF A, Accepted

* Equal Contribution, # Corresponding Author

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