HUTFormer: Hierarchical U-Net Transformer for Long-Term Traffic Forecasting
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909971261685760 |
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| author | Shao, Zezhi Wang, Fei Sun, Tao Yu, Chengqing Fang, Yuchen Jin, Guangyin An, Zhulin Liu, Yang Qu, Xiaobo Xu, Yongjun |
| author_facet | Shao, Zezhi Wang, Fei Sun, Tao Yu, Chengqing Fang, Yuchen Jin, Guangyin An, Zhulin Liu, Yang Qu, Xiaobo Xu, Yongjun |
| contents | Traffic forecasting, which aims to predict traffic conditions based on historical observations, has been an enduring research topic and is widely recognized as an essential component of intelligent transportation. Recent proposals on Spatial-Temporal Graph Neural Networks~(STGNNs) have made significant progress by combining sequential models with graph convolution networks. However, due to high complexity issues, STGNNs only focus on short-term traffic forecasting (e.g., 1-h ahead), while ignoring more practical long-term forecasting. In this paper, we make the first attempt to explore long-term traffic forecasting (e.g., 1-day ahead). To this end, we first reveal its unique challenges in exploiting multi-scale representations. Then, we propose a novel Hierarchical U-Net TransFormer~(HUTFormer) to address the issues of long-term traffic forecasting. HUTFormer consists of a hierarchical encoder and decoder to jointly generate and utilize multi-scale representations of traffic data. Specifically, for the encoder, we {\color{black}propose} window self-attention and segment merging to extract multi-scale representations from long-term traffic data. For the decoder, we design a cross-scale attention mechanism to effectively incorporate multi-scale representations. In addition, HUTFormer employs an efficient input embedding strategy to address the complexity issues. Extensive experiments on four traffic datasets show that the proposed HUTFormer significantly outperforms state-of-the-art traffic forecasting and long time series forecasting baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_14596 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | HUTFormer: Hierarchical U-Net Transformer for Long-Term Traffic Forecasting Shao, Zezhi Wang, Fei Sun, Tao Yu, Chengqing Fang, Yuchen Jin, Guangyin An, Zhulin Liu, Yang Qu, Xiaobo Xu, Yongjun Machine Learning Traffic forecasting, which aims to predict traffic conditions based on historical observations, has been an enduring research topic and is widely recognized as an essential component of intelligent transportation. Recent proposals on Spatial-Temporal Graph Neural Networks~(STGNNs) have made significant progress by combining sequential models with graph convolution networks. However, due to high complexity issues, STGNNs only focus on short-term traffic forecasting (e.g., 1-h ahead), while ignoring more practical long-term forecasting. In this paper, we make the first attempt to explore long-term traffic forecasting (e.g., 1-day ahead). To this end, we first reveal its unique challenges in exploiting multi-scale representations. Then, we propose a novel Hierarchical U-Net TransFormer~(HUTFormer) to address the issues of long-term traffic forecasting. HUTFormer consists of a hierarchical encoder and decoder to jointly generate and utilize multi-scale representations of traffic data. Specifically, for the encoder, we {\color{black}propose} window self-attention and segment merging to extract multi-scale representations from long-term traffic data. For the decoder, we design a cross-scale attention mechanism to effectively incorporate multi-scale representations. In addition, HUTFormer employs an efficient input embedding strategy to address the complexity issues. Extensive experiments on four traffic datasets show that the proposed HUTFormer significantly outperforms state-of-the-art traffic forecasting and long time series forecasting baselines. |
| title | HUTFormer: Hierarchical U-Net Transformer for Long-Term Traffic Forecasting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2307.14596 |