研究

论文列表

* 表示共同贡献。点击配图可查看大图。

SDFlow: Similarity-Driven Flow Matching for Time Series Generation

Li, W.*, Feng, S.*, Wu, P., Gao, X., Wu, M., & Zhao, P.
Conference on Neural Information Processing Systems (NeurIPS 2026)
CCF-A正式论文海报展示第一作者

SDFlow 在高维离散潜空间中引入相似性驱动的非自回归流匹配框架。通过学习低秩子空间，并利用相似性引导的流形锚点初始化生成过程，使生成过程与真实时序数据的几何结构相适应，支持并行合成。

arXiv
BibTeX
@misc{li2026sdflow,
      title={SDFlow: Similarity-Driven Flow Matching for Time Series Generation}, 
      author={Li, Wei and Feng, Shibo and Wu, Pengcheng and Gao, Xingyu and Wu, Min and Zhao, Peilin},
      year={2026},
      eprint={2605.05736},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2605.05736}, 
}

Adaptive Prototypical Contrastive Learning for Time Series Clustering

Li, W.
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2026)
CCF-A正式论文海报展示独立作者

面向聚类数量未知的时间序列聚类，结合层次原型、对比学习与最小描述长度（MDL）原则，联合学习数据表征与聚类数量。

ACM DL代码研究介绍
BibTeX
@inproceedings{li2026apcl,
  title={Adaptive Prototypical Contrastive Learning for Time Series Clustering},
  author={Li, Wei},
  booktitle={ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  year={2026},
  doi={10.1145/3770855.3817773},
  url={https://dl.acm.org/doi/10.1145/3770855.3817773}
}

ClusterPatchTST: Uncertainty-Aware Causal Clustering for Heterogeneous Time Series Forecasting

Li, W.
International Conference on Database Systems for Advanced Applications (DASFAA 2026)
CCF-B正式论文独立作者

利用因果聚类刻画跨序列的结构异质性，并通过不确定性建模提升预测的稳健性与可靠性。

DOI
BibTeX
@inproceedings{li2026clusterpatchtst,
  title={ClusterPatchTST: Uncertainty-Aware Causal Clustering for Heterogeneous Time Series Forecasting},
  author={Li, Wei},
  booktitle={International Conference on Database Systems for Advanced Applications},
  year={2026}
}

EnergyPatchTST: Multi-scale Time Series Transformers with Uncertainty Estimation for Energy Forecasting

Li, W., Wang, Z., Sun, Q., Gao, Q., & Yang, F.
International Conference on Intelligent Computing (ICIC 2025)
CCF-C口头报告第一作者

结合多尺度 Patch Transformer 与不确定性估计进行能源预测；相关平台已取得软件著作权，GitHub 获得 300 余颗星标。

DOI代码
BibTeX
@inproceedings{li2025energypatchtst,
  title={EnergyPatchTST: Multi-scale Time Series Transformers with Uncertainty Estimation for Energy Forecasting},
  author={Li, Wei and Wang, Zixin and Sun, Qizheng and Gao, Qixiang and Yang, Fenglei},
  booktitle={International Conference on Intelligent Computing},
  pages={319--330},
  year={2025},
  organization={Springer}
}

ScatterFusion: A Hierarchical Scattering Transform Framework for Enhanced Time Series Forecasting

Li, W.
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2026)
CCF-B独立作者

结合层次散射变换与多尺度融合，在噪声和尺度变化条件下提取稳定的时频表征。

DOI
BibTeX
@inproceedings{li2026scatterfusion,
  title={ScatterFusion: A Hierarchical Scattering Transform Framework for Enhanced Time Series Forecasting},
  author={Li, Wei},
  booktitle={IEEE International Conference on Acoustics, Speech, and Signal Processing},
  year={2026}
}

AWGFormer: Adaptive Wavelet-Guided Transformer for Multi-resolution Time Series Forecasting

Li, W.
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2026)
CCF-B独立作者

将自适应小波引导与 Transformer 相结合，用于多分辨率时间序列预测。

DOI
BibTeX
@inproceedings{li2026awgformer,
  title={AWGFormer: Adaptive Wavelet-Guided Transformer for Multi-resolution Time Series Forecasting},
  author={Li, Wei},
  booktitle={IEEE International Conference on Acoustics, Speech, and Signal Processing},
  year={2026}
}

SWIFT: State-space Wavelet Integrated Forecasting Technology for Enhanced Time Series Prediction

Li, W.
International Conference on Artificial Neural Networks (ICANN 2025)
CCF-C口头报告独立作者

结合状态空间建模与基于小波的多尺度模块，提升时间序列预测表现。

DOI代码
BibTeX
@inproceedings{li2025swift,
  title={SWIFT: State-Space Wavelet Integrated Forecasting Technology for Enhanced Time Series Prediction},
  author={Li, Wei},
  booktitle={International Conference on Artificial Neural Networks},
  pages={29--40},
  year={2025},
  organization={Springer}
}

TimeFlowDiffuser: A Hierarchical Diffusion Framework with Adaptive Context Sampling for Multi-Horizon Time Series Forecasting

Li, W.
International Conference on Artificial Neural Networks (ICANN 2025)
CCF-C口头报告独立作者

提出具有自适应上下文采样机制的层次扩散框架，用于多预测跨度的时间序列预测。

DOI
BibTeX
@inproceedings{li2025timeflowdiffuser,
  title={TimeFlowDiffuser: A Hierarchical Diffusion Framework with Adaptive Context Sampling for Multi-horizon Time Series Forecasting},
  author={Li, Wei},
  booktitle={International Conference on Artificial Neural Networks},
  pages={241--252},
  year={2025},
  organization={Springer}
}

LWSpace: Multi-Scale State Space Framework for Time Series Forecasting

Li, W.
International Conference on Intelligent Computing (ICIC 2025)
CCF-C口头报告独立作者

提出多尺度状态空间框架，用于高效时间序列预测。

DOI
BibTeX
@inproceedings{li2025lwspace,
  title={LWSpace: A Multi-scale State Space Framework for Enhanced Time Series Forecasting},
  author={Li, Wei},
  booktitle={International Conference on Intelligent Computing},
  pages={295--306},
  year={2025},
  organization={Springer}
}

Olympic Medal Prediction via Adaptive Triple-Fusion: Combining AAC Model with ARIMA-State Space Dynamics

Li, W., Wang, Z., Gao, Q., & Yang, F.
International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2025)
EI Index海报展示第一作者

结合自适应三重融合建模与 ARIMA 状态空间动力学，开展奥运奖牌预测。

DOI
BibTeX
@inproceedings{li2025olympic,
  title={Olympic Medal Prediction via Adaptive Triple-Fusion: Combining AAC Model with ARIMA-State Space Dynamics},
  author={Li, Wei and Wang, Zixin and Gao, Qixiang and Yang, Fenglei},
  booktitle={Proceedings of the 2025 5th International Conference on Applied Mathematics, Modelling and Intelligent Computing},
  pages={699--703},
  year={2025}
}