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Advanced Research in Spatial-Temporal Data Mining: Theory, Algorithms, and Applications

This special issue belongs to the section “E1: Mathematics and Computer Science“.

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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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Mathematics - ISSN 2227-7390