[1] 2023_AAAI_Spatio-Temporal Meta-Graph Learning for Traffic Forecasting. (MegaCRN, Rank CCF-A)

[2] 2024_KDD_Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting. (HimNet, Rank CCF-A)

[3] 2023_CIKM_STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting. (STAEFormer, Rank CCF-B)

[4] 2022_CIKM_Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting. (STID, Rank CCF-B)

[5] 2021_TKDE_Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting. (ASTGNN, Rank CCF-A)

[6] 2023_PR_A Decomposition Dynamic Graph Convolutional Recurrent Network for Traffic Forecasting. (DDGCRN, Rank CCF-B)

[7] 2023_TKDD_Dynamic Graph Convolutional Recurrent Network for Traffic Prediction. (DGCRN, Rank CCF-B)

[8] 2024_ICASSP_Dynamic Frequency Domain Graph Convolutional Network for Traffic Forecasting. (DFDGCN, Rank CCF-B)

[9] 2020_KDD_Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks. (MTGNN, Rank CCF-A)

[10] 2024_MDM_Spatial-Temporal Large Language Model for Traffic Prediction. (ST-LLM, Rank CCF-C)

[11] 2024_ICLR_TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Experts. (TESTAM, Default Rank CCF-A)

[12] 2024_IJCAI_Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting (STD-MAE, Rank CCF-A).

[13] 2023_AAAI_PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction.

[14] 2022_KDD_Pre-training-Enhanced Spatial-Temporal Graph Neural Network For Multivariate Time Series Forecasting.

[15] 2023_CIKM_Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting.

The Conference and Journal Abbreviations of the above papers refer to Directory of International Academic Conferences and Journals Recommended by China Computer Federation (CCF).