69 lines
3.6 KiB
Markdown
69 lines
3.6 KiB
Markdown
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# PatchTST
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## Overview
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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 and Jayant Kalagnanam.
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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 illustrated in the following figure:
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The abstract from the paper is the following:
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*We propose an efficient design of Transformer-based models for multivariate time series forecasting and self-supervised representation learning. It 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. 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. We also apply our model to self-supervised pre-training tasks and attain excellent fine-tuning performance, which outperforms supervised training on large datasets. Transferring of masked pre-trained representation on one dataset to others also produces SOTA forecasting accuracy.*
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This model was contributed by [namctin](https://huggingface.co/namctin), [gsinthong](https://huggingface.co/gsinthong), [diepi](https://huggingface.co/diepi), [vijaye12](https://huggingface.co/vijaye12), [wmgifford](https://huggingface.co/wmgifford), and [kashif](https://huggingface.co/kashif). The original code can be found [here](https://github.com/yuqinie98/PatchTST).
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## Usage tips
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The model can also be used for time series classification and time series regression. See the respective [`PatchTSTForClassification`] and [`PatchTSTForRegression`] classes.
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## Resources
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- A blog post explaining PatchTST in depth can be found [here](https://huggingface.co/blog/patchtst). The blog can also be opened in Google Colab.
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## PatchTSTConfig
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[[autodoc]] PatchTSTConfig
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## PatchTSTModel
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[[autodoc]] PatchTSTModel
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- forward
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## PatchTSTForPrediction
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[[autodoc]] PatchTSTForPrediction
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- forward
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## PatchTSTForClassification
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[[autodoc]] PatchTSTForClassification
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- forward
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## PatchTSTForPretraining
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[[autodoc]] PatchTSTForPretraining
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- forward
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## PatchTSTForRegression
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[[autodoc]] PatchTSTForRegression
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- forward
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