<div class="csl-bib-body">
<div class="csl-entry">Wang, P., Zhang, T., Zhang, H., Cheng, S., & Wang, W. (2023). Adding attention to the neural ordinary differential equation for spatio-temporal prediction. <i>International Journal of Geographical Information Science</i>. https://doi.org/10.1080/13658816.2023.2275160</div>
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dc.identifier.issn
1365-8816
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/189589
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dc.description.abstract
Explainable spatio-temporal prediction gains attraction in the development of geospatial artificial intelligence. The neural ordinal differential equation (NODE) emerges as a new solution for explainable spatio-temporal prediction. However, challenges still need to be solved in most existing NODE-based prediction models, such as difficulty modeling spatial data and mining long-term temporal dependencies in data. In this study, we propose a spatio-temporal attentional NODE (STA-ODE) to address the two challenges above. First, we define a spatio-temporal ordinary differential equation to predict a value at each time iteratively by a novel spatio-temporal derivative network. Second, we develop an attention mechanism to fuse multiple prediction values for capturing long-term temporal dependencies in data. To train the STA-ODE model, we design a loss function that aligns the prediction results in spatial dimension with prediction results in temporal dimension to calibrate the parameters of the model. The proposed model was validated with three real-world spatio-temporal datasets (traffic flow dataset, PM2.5 monitoring dataset, and temperature monitoring dataset). Experimental results showed that STA-ODE outperformed seven existing baselines regarding prediction accuracy. In addition, we used visualization to demonstrate the sound interpretability and prediction accuracy of the STA-ODE model.
en
dc.language.iso
en
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dc.publisher
TAYLOR & FRANCIS LTD
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dc.relation.ispartof
International Journal of Geographical Information Science
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dc.subject
Geospatial artificial intelligence
en
dc.subject
spatio-temporal prediction
en
dc.subject
spatio-temporal attention
en
dc.subject
neural ordinary differential equation
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dc.title
Adding attention to the neural ordinary differential equation for spatio-temporal prediction
en
dc.type
Article
en
dc.type
Artikel
de
dc.contributor.affiliation
Wuhan University, China
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dc.contributor.affiliation
Wuhan University, China
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dc.contributor.affiliation
University of Chinese Academy of Sciences, China
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dc.contributor.affiliation
Chinese Academy of Sciences, China
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dc.type.category
Original Research Article
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tuw.journal.peerreviewed
true
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tuw.peerreviewed
true
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wb.publication.intCoWork
International Co-publication
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tuw.researchTopic.id
I5
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tuw.researchTopic.name
Visual Computing and Human-Centered Technology
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tuw.researchTopic.value
100
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dcterms.isPartOf.title
International Journal of Geographical Information Science