POLLA: Enhancing the Local Structure Awareness in Long Sequence Spatial-temporal Modeling

This is the pytorch implementation of POLLA in the ACM TIST'21 paper: POLLA: Enhancing the Local Structure Awareness in Long Sequence Spatial-temporal Modeling.

Requirements

• Python 3.6
• matplotlib == 3.1.1
• numpy == 1.19.4
• pandas == 0.25.1
• scikit_learn == 0.21.3
• torch == 1.8.0
• ...

Dependencies can be installed using the following command:

pip install -r requirements.txt

Train Commands

Commands for training model on METR-LA:

python -u main_polla_exp.py --model polladiff --data metr --seq_len 12 --pred_len 12 --d_model 64 --n_layers 3 --n_heads 8 --d_ff 256 --train_epochs 4 --patience 10 --itr 2 --loss mae

Citation

If you find this repository useful in your research, please consider citing the following paper:

@article{haoyietal-polla-2021,
  author    = {Haoyi Zhou and
               Hao Peng and
               Jieqi Peng and
               Shuai Zhang and
               Jianxin Li},
  title     = {{POLLA:} Enhancing the Local Structure Awareness in Long Sequence
               Spatial-temporal Modeling},
  journal   = {{ACM} Transactions on Intelligent Systems and Technology},
  volume    = {12},
  number    = {6},
  pages     = {69:1--69:24},
  year      = {2021},
  doi       = {10.1145/3447987},
}

Contact

If you have any questions, feel free to contact Haoyi Zhou through email ([email protected]) or Github issues. Pull requests are highly welcomed!