---
tags:
- generated_from_trainer
- time series
- forecasting
- pretrained models
- foundation models
- time series foundation models
- time-series
license: apache-2.0
pipeline_tag: time-series-forecasting
model-index:
- name: patchtst_etth1_forecast
  results: []
---

# PatchTST model pre-trained on ETTh1 dataset

<!-- Provide a quick summary of what the model is/does. -->

[`PatchTST`](https://huggingface.co/docs/transformers/model_doc/patchtst) is a transformer-based model for time series modeling tasks, including forecasting, regression, and classification. This repository contains a pre-trained `PatchTST` model encompassing all seven channels of the `ETTh1` dataset. 
This particular pre-trained model produces a Mean Squared Error (MSE) of 0.3881 on the `test` split of the `ETTh1` dataset when forecasting 96 hours into the future with a historical data window of 512 hours.

For training and evaluating a `PatchTST` model, you can refer to this [demo notebook](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/patch_tst_getting_started.ipynb).

## Model Details

### Model Description

The `PatchTST` model was proposed in A Time Series is Worth [64 Words: Long-term Forecasting with Transformers](https://arxiv.org/abs/2211.14730) by Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, Jayant Kalagnanam.

At a high level the model vectorizes time series into patches of a given size and encodes the resulting sequence of vectors via a Transformer that then outputs the prediction length forecast via an appropriate head.

The model is based on two key components: (i) segmentation of time series into subseries-level patches which are served as input tokens to Transformer; (ii) channel-independence where each channel contains a single univariate time series that shares the same embedding and Transformer weights across all the series. The patching design naturally has three-fold benefit: local semantic information is retained in the embedding; computation and memory usage of the attention maps are quadratically reduced given the same look-back window; and the model can attend longer history. Our channel-independent patch time series Transformer (PatchTST) can improve the long-term forecasting accuracy significantly when compared with that of SOTA Transformer-based models.

In addition, PatchTST has a modular design to seamlessly support masked time series pre-training as well as direct time series forecasting, classification, and regression.

<img src="patchtst_architecture.png" alt="Architecture" width="600" />

### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** [PatchTST Hugging Face](https://huggingface.co/docs/transformers/model_doc/patchtst)
- **Paper:** [PatchTST ICLR 2023 paper](https://dl.acm.org/doi/abs/10.1145/3580305.3599533)
- **Demo:** [Get started with PatchTST](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/patch_tst_getting_started.ipynb)

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
This pre-trained model can be employed for fine-tuning or evaluation using any Electrical Transformer dataset that has the same channels as the `ETTh1` dataset, specifically: `HUFL, HULL, MUFL, MULL, LUFL, LULL, OT`. The model is designed to predict the next 96 hours based on the input values from the preceding 512 hours. It is crucial to normalize the data. For a more comprehensive understanding of data pre-processing, please consult the paper or the demo.

## How to Get Started with the Model

Use the code below to get started with the model.

[Demo](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/patch_tst_getting_started.ipynb)

## Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**
```
@misc{nie2023time,
      title={A Time Series is Worth 64 Words: Long-term Forecasting with Transformers}, 
      author={Yuqi Nie and Nam H. Nguyen and Phanwadee Sinthong and Jayant Kalagnanam},
      year={2023},
      eprint={2211.14730},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
```

**APA:**
```
Nie, Y., Nguyen, N., Sinthong, P., & Kalagnanam, J. (2023). A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. arXiv preprint arXiv:2211.14730.
```