| PatchTST | |
| Overview | |
| The PatchTST model was proposed in A Time Series is Worth 64 Words: Long-term Forecasting with Transformers by Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong and 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 illustrated in the following figure: | |
| The abstract from the paper is the following: | |
| 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. | |
| This model was contributed by namctin, gsinthong, diepi, vijaye12, wmgifford, and kashif. The original code can be found here. | |
| Usage tips | |
| The model can also be used for time series classification and time series regression. See the respective [PatchTSTForClassification] and [PatchTSTForRegression] classes. | |
| Resources | |
| A blog post explaining PatchTST in depth can be found here. The blog can also be opened in Google Colab. | |
| PatchTSTConfig | |
| [[autodoc]] PatchTSTConfig | |
| PatchTSTModel | |
| [[autodoc]] PatchTSTModel | |
| - forward | |
| PatchTSTForPrediction | |
| [[autodoc]] PatchTSTForPrediction | |
| - forward | |
| PatchTSTForClassification | |
| [[autodoc]] PatchTSTForClassification | |
| - forward | |
| PatchTSTForPretraining | |
| [[autodoc]] PatchTSTForPretraining | |
| - forward | |
| PatchTSTForRegression | |
| [[autodoc]] PatchTSTForRegression | |
| - forward |