Research

Publications

* denotes equal contribution. Click a figure to enlarge it.

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-AFull PaperPosterFirst Author

SDFlow introduces a similarity-driven, non-autoregressive flow-matching framework for time-series generation in high-dimensional discrete latent spaces. By learning a low-rank subspace and initializing generation with similarity-guided manifold anchors, it aligns the generative process with the geometry of real temporal data and supports parallel synthesis.

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-AFull PaperPosterSole Author

Addresses time-series clustering with an unknown number of clusters by combining hierarchical prototypes, contrastive learning, and the MDL principle to jointly learn representations and cluster cardinality.

ACM DL Code Read the story

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-BFull PaperSole Author

Uses causal clustering to capture cross-series structural heterogeneity and uncertainty modeling to improve robust and reliable forecasting.

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-COralFirst Author

Combines multiscale Patch Transformers with uncertainty estimation for energy forecasting; the platform received a software copyright and 300+ GitHub stars.

DOI Code

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-BSole Author

Combines hierarchical scattering transforms with multiscale fusion to extract stable time-frequency representations under noise and scale variation.

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-BSole Author

This work integrates adaptive wavelet guidance with Transformers for multi-resolution time series forecasting.

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-COralSole Author

This work combines state-space modeling and wavelet-based multi-scale modules for enhanced time series forecasting.

DOI Code

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-COralSole Author

This work introduces a hierarchical diffusion framework with adaptive context sampling for multi-horizon time series forecasting.

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-COralSole Author

This work proposes a multi-scale state space framework for efficient time series forecasting.

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 IndexPosterFirst Author

This work combines adaptive triple-fusion modeling with ARIMA-state space dynamics for Olympic medal prediction.

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}
}