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-# 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: ynie@princeton.edu or nnguyen@us.ibm.com 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}
-}
-```
-
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