GestaltCogTeam / BasicTS

A Fair and Scalable Time Series Forecasting Benchmark and Toolkit.
https://ieeexplore.ieee.org/document/10726722/
Apache License 2.0
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TODOs & HOWTOs #95

Open zezhishao opened 1 year ago

zezhishao commented 1 year ago

ToDos

Done

huangst21 commented 12 months ago

您好,感谢您提供的框架。关于交通预测问题,或许您可以考虑添加上PEMS07(M)和PEMS07(L)这两个数据集?

zezhishao commented 12 months ago

您好,感谢您提供的框架。关于交通预测问题,或许您可以考虑添加上PEMS07(M)和PEMS07(L)这两个数据集?

Of course, could you provide me the source of these two datasets? E.g., links or papers.

huangst21 commented 12 months ago

您好,感谢您提供的框架。关于交通预测问题,或许您可以考虑添加上PEMS07(M)和PEMS07(L)这两个数据集?

Of course, could you provide me the source of these two datasets? E.g., links or papers.

STGCN的库里包含了这两个速度数据集: https://github.com/VeritasYin/STGCN_IJCAI-18

zezhishao commented 11 months ago

@all-contributors please add @zezhishao for maintenance,infra,code,bug. please add @LMissher for code,bug. please add @cnstark for infra. please add @Azusa-Yuan for bug. please add @ywoelker for bug. please add @hlhang9527 for bug.

littleheuer commented 8 months ago

How to set AMP?

zezhishao commented 8 months ago

How to set AMP?

Currently, BasicTS does not support AMP because these baselines don’t have many parameters. However, I have also recently tested AMP in BasicTS to facilitate training and inference of large time series models. AMP will be added in the future (probably next version).

If you are want to use AMP, you need to init torch.amp.autocast and torch.cuda.amp.GradScaler in the __init__ function of the runner, and then modify the train_iters, val_iters, test, and backward function.

WaldenBlues commented 4 months ago

It seems that STD-MAE [2024-LJCAI] have mentioned this library. Maybe it time to put that into this repo?

zezhishao commented 1 month ago

It seems that STD-MAE [2024-LJCAI] have mentioned this library. Maybe it time to put that into this repo?

Hi, the STD-MAE has been added. Enjoy! If BasicTS does help you, please give us a star and consider citing this paper:

@article{shao2023exploring,
   title={Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis},
   author={Shao, Zezhi and Wang, Fei and Xu, Yongjun and Wei, Wei and Yu, Chengqing and Zhang, Zhao and Yao, Di and Jin, Guangyin and Cao, Xin and Cong, Gao and others},
   journal={arXiv preprint arXiv:2310.06119},
   year={2023}
 }

Thanks a lot!