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https://github.com/yuqinie98/PatchTST
An offical implementation of PatchTST: "A Time Series is Worth 64 Words: Long-term Forecasting with Transformers." (ICLR 2023) https://arxiv.org/abs/2211.14730
https://github.com/yuqinie98/PatchTST
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An offical implementation of PatchTST: "A Time Series is Worth 64 Words: Long-term Forecasting with Transformers." (ICLR 2023) https://arxiv.org/abs/2211.14730
- Host: GitHub
- URL: https://github.com/yuqinie98/PatchTST
- Owner: yuqinie98
- License: apache-2.0
- Created: 2022-10-25T08:55:07.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-25T06:43:15.000Z (11 months ago)
- Last Synced: 2024-08-02T06:19:47.732Z (6 months ago)
- Language: Python
- Homepage:
- Size: 12.6 MB
- Stars: 1,436
- Watchers: 13
- Forks: 255
- Open Issues: 39
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-time-series - [code
README
# PatchTST (ICLR 2023)
### This is an offical implementation of PatchTST: [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://arxiv.org/abs/2211.14730).
:triangular_flag_on_post: Our model has been included in [GluonTS](https://github.com/awslabs/gluonts). Special thanks to the contributor @[kashif](https://github.com/kashif)!
:triangular_flag_on_post: Our model has been included in [NeuralForecast](https://github.com/Nixtla/neuralforecast). Special thanks to the contributor @[kdgutier](https://github.com/kdgutier) and @[cchallu](https://github.com/cchallu)!
:triangular_flag_on_post: Our model has been included in [timeseriesAI(tsai)](https://github.com/timeseriesAI/tsai/blob/main/tutorial_nbs/15_PatchTST_a_new_transformer_for_LTSF.ipynb). Special thanks to the contributor @[oguiza](https://github.com/oguiza)!
We offer a video that provides a concise overview of our paper for individuals seeking a rapid comprehension of its contents: https://www.youtube.com/watch?v=Z3-NrohddJw
## Key Designs
:star2: **Patching**: segmentation of time series into subseries-level patches which are served as input tokens to Transformer.
:star2: **Channel-independence**: each channel contains a single univariate time series that shares the same embedding and Transformer weights across all the series.
## Results
### Supervised Learning
Compared with the best results that Transformer-based models can offer, PatchTST/64 achieves an overall **21.0%** reduction on MSE and **16.7%** reduction
on MAE, while PatchTST/42 attains a overall **20.2%** reduction on MSE and **16.4%** reduction on MAE. It also outperforms other non-Transformer-based models like DLinear.
### Self-supervised Learning
We do comparison with other supervised and self-supervised models, and self-supervised PatchTST is able to outperform all the baselines.
We also test the capability of transfering the pre-trained model to downstream tasks.
## Efficiency on Long Look-back Windows
Our PatchTST consistently reduces the MSE scores as the look-back window increases, which confirms our model’s capability to learn from longer receptive field.
## Getting Started
We seperate our codes for supervised learning and self-supervised learning into 2 folders: ```PatchTST_supervised``` and ```PatchTST_self_supervised```. Please choose the one that you want to work with.
### Supervised Learning
1. Install requirements. ```pip install -r requirements.txt```
2. Download data. You can download all the datasets from [Autoformer](https://drive.google.com/drive/folders/1ZOYpTUa82_jCcxIdTmyr0LXQfvaM9vIy). Create a seperate folder ```./dataset``` and put all the csv files in the directory.
3. Training. All the scripts are in the directory ```./scripts/PatchTST```. The default model is PatchTST/42. For example, if you want to get the multivariate forecasting results for weather dataset, just run the following command, and you can open ```./result.txt``` to see the results once the training is done:
```
sh ./scripts/PatchTST/weather.sh
```
You can adjust the hyperparameters based on your needs (e.g. different patch length, different look-back windows and prediction lengths.). We also provide codes for the baseline models.
### Self-supervised Learning
1. Follow the first 2 steps above
2. Pre-training: The scirpt patchtst_pretrain.py is to train the PatchTST/64. To run the code with a single GPU on ettm1, just run the following command
```
python patchtst_pretrain.py --dset ettm1 --mask_ratio 0.4
```
The model will be saved to the saved_model folder for the downstream tasks. There are several other parameters can be set in the patchtst_pretrain.py script.
3. Fine-tuning: The script patchtst_finetune.py is for fine-tuning step. Either linear_probing or fine-tune the entire network can be applied.
```
python patchtst_finetune.py --dset ettm1 --pretrained_model
```
## Acknowledgement
We appreciate the following github repo very much for the valuable code base and datasets:
https://github.com/cure-lab/LTSF-Linear
https://github.com/zhouhaoyi/Informer2020
https://github.com/thuml/Autoformer
https://github.com/MAZiqing/FEDformer
https://github.com/alipay/Pyraformer
https://github.com/ts-kim/RevIN
https://github.com/timeseriesAI/tsai
## Contact
If you have any questions or concerns, please contact us: [email protected] or [email protected] or submit an issue
## Citation
If you find this repo useful in your research, please consider citing our paper as follows:
```
@inproceedings{Yuqietal-2023-PatchTST,
title = {A Time Series is Worth 64 Words: Long-term Forecasting with Transformers},
author = {Nie, Yuqi and
H. Nguyen, Nam and
Sinthong, Phanwadee and
Kalagnanam, Jayant},
booktitle = {International Conference on Learning Representations},
year = {2023}
}
```