Decoupling Long- and Short-Term Patterns in Spatiotemporal Inference

• URL: http://arxiv.org/abs/2109.09506v3
• Date: Tue, 23 Apr 2024 14:18:38 GMT
• Title: Decoupling Long- and Short-Term Patterns in Spatiotemporal Inference
• Authors: Junfeng Hu, Yuxuan Liang, Zhencheng Fan, Li Liu, Yifang Yin, Roger Zimmermann,
• Abstract summary: It is impractical to deploy massive sensors due to the expensive costs. How to get fine-grained data measurement has long been a pressing issue. We propose a graphtemporal attention network to learn the relations across space and time for short-term patterns.
• Score: 31.245426664456257
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sensors are the key to environmental monitoring, which impart benefits to smart cities in many aspects, such as providing real-time air quality information to assist human decision-making. However, it is impractical to deploy massive sensors due to the expensive costs, resulting in sparse data collection. Therefore, how to get fine-grained data measurement has long been a pressing issue. In this paper, we aim to infer values at non-sensor locations based on observations from available sensors (termed spatiotemporal inference), where capturing spatiotemporal relationships among the data plays a critical role. Our investigations reveal two significant insights that have not been explored by previous works. Firstly, data exhibits distinct patterns at both long- and short-term temporal scales, which should be analyzed separately. Secondly, short-term patterns contain more delicate relations including those across spatial and temporal dimensions simultaneously, while long-term patterns involve high-level temporal trends. Based on these observations, we propose to decouple the modeling of short-term and long-term patterns. Specifically, we introduce a joint spatiotemporal graph attention network to learn the relations across space and time for short-term patterns. Furthermore, we propose a graph recurrent network with a time skip strategy to alleviate the gradient vanishing problem and model the long-term dependencies. Experimental results on four public real-world datasets demonstrate that our method effectively captures both long- and short-term relations, achieving state-of-the-art performance against existing methods.

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