Decoupling Long-and Short-Term Patterns in Spatiotemporal Inference

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Decoupling Long-and Short-Term Patterns in Spatiotemporal Inference
Title:
Decoupling Long-and Short-Term Patterns in Spatiotemporal Inference
Journal Title:
IEEE Transactions on Neural Networks and Learning Systems
Publication Date:
21 July 2023
Citation:
Hu, J., Liang, Y., Fan, Z., Liu, L., Yin, Y., & Zimmermann, R. (2024). Decoupling Long-and Short-Term Patterns in Spatiotemporal Inference. IEEE Transactions on Neural Networks and Learning Systems, 1–13. https://doi.org/10.1109/tnnls.2023.3293814
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.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Ministry of Education - Academic Research Fund Tier 2
Grant Reference no. : T2EP20221-0023

It is also supported by Guangzhou Municipal Science and Technology Project 2023A03J0011.
Description:
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2162-237X
2162-2388
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