README.md

One Fits All: Power General Time Series Analysis by Pretrained LM (NeurIPS 2023 Spotlight)

Tian Zhou, Peisong Niu, Xue Wang, Liang Sun, Rong Jin, "One Fits All: Power General Time Series Analysis by Pretrained LM,", NeurIPS, 2023. [paper]

Long-term Learning

Get Start

• The code is the same as few-shot leanring with 100 percent.
• Install Python>=3.8, PyTorch 1.8.1.
• Download data. You can obtain all the benchmarks from [TimesNet].
• For electricity and traffic datasets with a batch size of 2048, we utilize 4 V100 GPUs, while for other datasets, we use a single V100 GPU.
• Train the model. We provide the experiment scripts of all benchmarks under the folder ./scripts. You can reproduce the experiment results by:

bash ./scripts/ETTh1.sh
bash ./scripts/ETTh2.sh

Citation

If you find this repo useful, please cite our paper.

@inproceedings{zhou2023onefitsall,
  title={{One Fits All}: Power General Time Series Analysis by Pretrained LM},
  author={Tian Zhou, Peisong Niu, Xue Wang, Liang Sun, Rong Jin},
  booktitle={NeurIPS},
  year={2023}
}