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https://github.com/thuml/Autoformer

About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008
https://github.com/thuml/Autoformer

deep-learning time-series

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About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008

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# Autoformer (NeurIPS 2021)



Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting



Time series forecasting is a critical demand for real applications. Enlighted by the classic time series analysis and stochastic process theory, we propose the Autoformer as a general series forecasting model [[paper](https://arxiv.org/abs/2106.13008)]. **Autoformer goes beyond the Transformer family and achieves the series-wise connection for the first time.**



In long-term forecasting, Autoformer achieves SOTA, with a **38% relative improvement** on six benchmarks, covering five practical applications: **energy, traffic, economics, weather and disease**.



:triangular_flag_on_post:**News** (2023.08) Autoformer has been included in [Hugging Face](https://huggingface.co/models?search=autoformer). See [blog](https://huggingface.co/blog/autoformer).



:triangular_flag_on_post:**News** (2023.06) The extension version of Autoformer ([Interpretable weather forecasting for worldwide stations with a unified deep model](https://www.nature.com/articles/s42256-023-00667-9)) has been published in Nature Machine Intelligence as the [Cover Article](https://www.nature.com/natmachintell/volumes/5/issues/6).



:triangular_flag_on_post:**News** (2023.02) Autoformer has been included in our [[Time-Series-Library]](https://github.com/thuml/Time-Series-Library), which covers long- and short-term forecasting, imputation, anomaly detection, and classification.



:triangular_flag_on_post:**News** (2022.02-2022.03) Autoformer has been deployed in [2022 Winter Olympics](https://en.wikipedia.org/wiki/2022_Winter_Olympics) to provide weather forecasting for competition venues, including wind speed and temperature.



## Autoformer vs. Transformers



**1. Deep decomposition architecture**



We renovate the Transformer as a deep decomposition architecture, which can progressively decompose the trend and seasonal components during the forecasting process.











Figure 1. Overall architecture of Autoformer.




**2. Series-wise Auto-Correlation mechanism**



Inspired by the stochastic process theory, we design the Auto-Correlation mechanism, which can discover period-based dependencies and aggregate the information at the series level. This empowers the model with inherent log-linear complexity. This series-wise connection contrasts clearly from the previous self-attention family.











Figure 2. Auto-Correlation mechansim.




## Get Started



1. Install Python 3.6, PyTorch 1.9.0.

2. Download data. You can obtain all the six benchmarks from [Google Drive](https://drive.google.com/drive/folders/1ZOYpTUa82_jCcxIdTmyr0LXQfvaM9vIy?usp=sharing). **All the datasets are well pre-processed** and can be used easily.

3. Train the model. We provide the experiment scripts of all benchmarks under the folder `./scripts`. You can reproduce the experiment results by:



```bash

bash ./scripts/ETT_script/Autoformer_ETTm1.sh

bash ./scripts/ECL_script/Autoformer.sh

bash ./scripts/Exchange_script/Autoformer.sh

bash ./scripts/Traffic_script/Autoformer.sh

bash ./scripts/Weather_script/Autoformer.sh

bash ./scripts/ILI_script/Autoformer.sh

```



4. Special-designed implementation



- **Speedup Auto-Correlation:** We built the Auto-Correlation mechanism as a batch-normalization-style block to make it more memory-access friendly. See the [paper](https://arxiv.org/abs/2106.13008) for details.



- **Without the position embedding:** Since the series-wise connection will inherently keep the sequential information, Autoformer does not need the position embedding, which is different from Transformers.



### Reproduce with Docker



To easily reproduce the results using Docker, conda and Make,  you can follow the next steps:

1. Initialize the docker image using: `make init`. 

2. Download the datasets using: `make get_dataset`.

3. Run each script in `scripts/` using `make run_module module="bash scripts/ETT_script/Autoformer_ETTm1.sh"` for each script.

4. Alternatively, run all the scripts at once:

```

for file in `ls scripts`; do make run_module module="bash scripts/$script"; done

```

### A Simple Example

See `predict.ipynb` for workflow (in Chinese).



## Main Results



We experiment on six benchmarks, covering five main-stream applications. We compare our model with ten baselines, including Informer, N-BEATS, etc. Generally, for the long-term forecasting setting, Autoformer achieves SOTA, with a **38% relative improvement** over previous baselines.










## Baselines



We will keep adding series forecasting models to expand this repo:



- [x] Autoformer

- [x] Informer

- [x] Transformer

- [x] Reformer

- [ ] LogTrans

- [ ] N-BEATS



## Citation



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



```

@inproceedings{wu2021autoformer,

  title={Autoformer: Decomposition Transformers with {Auto-Correlation} for Long-Term Series Forecasting},

  author={Haixu Wu and Jiehui Xu and Jianmin Wang and Mingsheng Long},

  booktitle={Advances in Neural Information Processing Systems},

  year={2021}

}

```



## Contact



If you have any questions or want to use the code, please contact [email protected].



## Acknowledgement



We appreciate the following github repos a lot for their valuable code base or datasets:



https://github.com/zhouhaoyi/Informer2020



https://github.com/zhouhaoyi/ETDataset



https://github.com/laiguokun/multivariate-time-series-data