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Title	Spatiotemporal Data Prediction Model Based On A Multi-Layer Attention Mechanism
ID_Doc	52605
Authors	Jiang M.; Han Q.; Zhang H.; Liu H.
Year	2022
Published	International Journal of Data Warehousing and Mining, 19, 2
DOI	http://dx.doi.org/10.4018/IJDWM.315822
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 multilayer 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. © 2022 IGI Global. All rights reserved.
Author Keywords	Attention Mechanism; Data Mining; Dynamic Spatial Relationships; Encoder-Decoder; LSTM; Multiple Temporal Relationships; Smart Cities; Spatiotemporal Data Prediction

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