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[KDD'24] Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting

Our work is already accepted by KDD2024 research track. The citation information will be updated when the official proceeding is online. Arxiv link

method

Citation

@article{dong2024heterogeneity,
  title={Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting},
  author={Dong, Zheng and Jiang, Renhe and Gao, Haotian and Liu, Hangchen and Deng, Jinliang and Wen, Qingsong and Song, Xuan},
  journal={arXiv preprint arXiv:2405.10800},
  year={2024}
}

Performance on Spatiotemporal Forecasting Benchmarks

image

Required Packages

pytorch>=1.12
numpy
pandas
matplotlib
pyyaml
pickle
torchinfo

Training Commands

cd scripts/
python train.py -d <dataset> -g <gpu_id>

<dataset>:

  • METRLA
  • PEMSBAY
  • PEMS04
  • PEMS07
  • PEMS08

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