A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

• URL: http://arxiv.org/abs/2211.14730v1
• Date: Sun, 27 Nov 2022 05:15:42 GMT
• Title: A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
• Authors: Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, Jayant Kalagnanam
• Abstract summary: We propose an efficient design of Transformer-based models for 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. PatchTST can improve the long-term forecasting accuracy significantly when compared with that of SOTA Transformer-based models.
• Score: 4.635547236305835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Code is available at: https://github.com/yuqinie98/PatchTST.

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