Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

This is the origin Pytorch implementation of Informer in the following paper: Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. Special thanks to Jieqi Peng@cookieminions for building this repo.

Figure 1. The architecture of Informer.

Requeirements

Python 3.6
matplotlib == 3.1.1
numpy == 1.17.3
pandas == 0.25.1
scikit_learn == 0.21.3
torch == 1.2.0

Dependencies can be installed using the following command:

pip install -r requirements.txt

Data

The ETT dataset used in the paper can be download in the repo ETDataset. The required data files should be put into data/ETT/ folder. A demo slice of the ETT data is illustrated in the following figure. Note that the input of each dataset is zero-mean normalized in this implementation.

Figure 2. A demo of the ETT data.

Usage

Commands for training and testing the model with ProbSparse self-attention on Dataset ETTh1, ETTh2 and ETTm1 respectively:

# ETTh1
python -u main_informer.py --model informer --data ETTh1 --attn prob

# ETTh2
python -u main_informer.py --model informer --data ETTh2 --attn prob

# ETTm1
python -u main_informer.py --model informer --data ETTm1 --attn prob

More parameter information please refer to main_informer.py.

Results

Figure 3. Univariate forecasting results.

Figure 4. Multivariate forecasting results.

Citation

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

@inproceedings{haoyietal-informer-2021,
  author    = {Haoyi Zhou and
               Shanghang Zhang and
               Jieqi Peng and
               Shuai Zhang and
               Jianxin Li and
               Hui Xiong and
               Wancai Zhang},
  title     = {Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting},
  booktitle = {The Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI} 2021},
  pages     = {online},
  publisher = {{AAAI} Press},
  year      = {2021},
}

youjp / Informer2020