Spatiotemporal Data Prediction Model Based on a Multi-Layer Attention Mechanism

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Author(s): Man Jiang (Harbin Engineering University, China), Qilong Han (Harbin Engineering Unviversity, China), Haitao Zhang (Harbin Engineering University)and Hexiang Liu (Harbin Engineering University, China)
Copyright: 2023
Volume: 19
Issue: 2
Pages: 15
Source title: International Journal of Data Warehousing and Mining (IJDWM)
Editor(s)-in-Chief: Eric Pardede (La Trobe University, Australia)and Kiki Adhinugraha (La Trobe University, Australia)
DOI: 10.4018/IJDWM.315822

Keywords: Data Mining / Data Mining and Databases / Information Science Reference / Library & Information Science

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Abstract

Spatiotemporal data prediction is of great significance in the fields of smart cities and smart manufacturing. Current spatiotemporal data prediction models heavily rely on traditional spatial views or single temporal granularity, which suffer from missing knowledge, including dynamic spatial correlations, periodicity, and mutability. This paper addresses these challenges by proposing a multi-layer attention-based predictive model. The key idea of this paper is to use a multi-layer attention mechanism to model the dynamic spatial correlation of different features. Then, multi-granularity historical features are fused to predict future spatiotemporal data. Experiments on real-world data show that the proposed model outperforms six state-of-the-art benchmark methods.

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Spatiotemporal Data Prediction Model Based on a Multi-Layer Attention Mechanism

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