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---
tags:
- generated_from_trainer
license: cdla-permissive-2.0
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)

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[`ETTh1`/train split](https://github.com/zhouhaoyi/ETDataset/blob/main/ETT-small/ETTh1.csv). 
Train/validation/test splits are shown in the [demo](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/patch_tst_getting_started.ipynb).


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training Results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.4306        | 1.0   | 1005  | 0.7268          |
| 0.3641        | 2.0   | 2010  | 0.7456          |
| 0.348         | 3.0   | 3015  | 0.7161          |
| 0.3379        | 4.0   | 4020  | 0.7428          |
| 0.3284        | 5.0   | 5025  | 0.7681          |
| 0.321         | 6.0   | 6030  | 0.7842          |
| 0.314         | 7.0   | 7035  | 0.7991          |
| 0.3088        | 8.0   | 8040  | 0.8021          |
| 0.3053        | 9.0   | 9045  | 0.8199          |
| 0.3019        | 10.0  | 10050 | 0.8173          |

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data

[`ETTh1`/test split](https://github.com/zhouhaoyi/ETDataset/blob/main/ETT-small/ETTh1.csv). 
Train/validation/test splits are shown in the [demo](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/patch_tst_getting_started.ipynb).

### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

Mean Squared Error (MSE).

### Results
It achieves a MSE of 0.3881 on the evaluation dataset.

#### Hardware

1 NVIDIA A100 GPU

#### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.14.1


## 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.
```