Advanced Research in Spatial-Temporal Data Mining: Theory, Algorithms, and Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 16 October 2026 | Viewed by 796
Special Issue Editor
Special Issue Information
Dear Colleagues,
In recent years, spatial-temporal data mining has emerged as a crucial research frontier, driven by the rapid growth of sensor networks, IoT devices, and real-time monitoring systems. The ability to effectively model and forecast complex spatial-temporal dynamics has profound implications for domains such as transportation, climate science, urban computing, healthcare, and industrial systems. However, challenges remain in capturing multi-scale dependencies, handling high-dimensional heterogeneous data, and ensuring robustness against anomalies and missing information.
This Special Issue aims to bring together cutting-edge research and novel methodologies in spatial-temporal data and multivariate time series data mining. We welcome original research papers, reviews, and communications that advance theory, algorithms, and applications in this area.
Topics of interest include, but are not limited to, the following:
- Spatial-temporal deep learning and graph neural networks;
- Dynamic representation learning for multi-variate time series;
- Foundation models and large-scale forecasting frameworks;
- Explainable and interpretable spatial-temporal models;
- Anomaly detection and root-cause analysis in complex systems;
- Real-world applications in transportation, energy, climate, and healthcare.
We encourage contributions that address emerging challenges, propose innovative solutions, and demonstrate the transformative potential of spatial-temporal data mining in real-world decision-making.
Dr. Xu Wang
Guest Editor
Manuscript Submission Information
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Keywords
- spatial-temporal deep learning and graph neural networks
- dynamic representation learning for multi-variate time series
- foundation models and large-scale forecasting frameworks
- explainable and interpretable spatial-temporal models
- anomaly detection and root-cause analysis in complex systems
- real-world applications in transportation, energy, climate, and healthcare
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