mlsanityData to 7 Oct 2026

A Survey of Generative Techniques for Spatial-Temporal Data Mining

arXiv 2405.09592 ↗First posted 15 May 2024cs.LG cs.AI cs.CEcited by 14

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Abstract (arXiv)

This paper focuses on the integration of generative techniques into spatial-temporal data mining, considering the significant growth and diverse nature of spatial-temporal data. With the advancements in RNNs, CNNs, and other non-generative techniques, researchers have explored their application in capturing temporal and spatial dependencies within spatial-temporal data. However, the emergence of generative techniques such as LLMs, SSL, Seq2Seq and diffusion models has opened up new possibilities for enhancing spatial-temporal data mining further. The paper provides a comprehensive analysis of generative technique-based spatial-temporal methods and introduces a standardized framework specifically designed for the spatial-temporal data mining pipeline. By offering a detailed review and a novel taxonomy of spatial-temporal methodology utilizing generative techniques, the paper enables a deeper understanding of the various techniques employed in this field. Furthermore, the paper highlights promising future research directions, urging researchers to delve deeper into spatial-temporal data mining. It emphasizes the need to explore untapped opportunities and push the boundaries of knowledge to unlock new insights and improve the effectiveness and efficiency of spatial-temporal data mining. By integrating generative techniques and providing a standardized framework, the paper contributes to advancing the field and encourages researchers to explore the vast potential of generative techniques in spatial-temporal data mining.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 122 of its 270 references that are in the map. First posted on arXiv 15 May 2024.

Builds on

122 of 270 references.

References · 122

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50 most cited of 122 All 122 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 5; 2026: 4.

14 papers.

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  1. 2024Mobility-LLM: Learning Visiting Intentions and Travel Preferences from Human Mobility Data with Large Language Modelscited by 21
  2. 2024A Survey on Point-of-Interest Recommendation: Models, Architectures, and Securitycited by 12
  3. 2024Self-Supervised Representation Learning for Geospatial Objects: A Surveycited by 8
  4. 2025AutoHFormer: Efficient Hierarchical Autoregressive Transformer for Time Series Predictioncited by 7
  5. 2026STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learningcited by 7
  6. 2024DiffLight: A Partial Rewards Conditioned Diffusion Model for Traffic Signal Control with Missing Datacited by 4
  7. 2025FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulationcited by 4
  8. 2025FLDmamba: Integrating Fourier and Laplace Transform Decomposition with Mamba for Enhanced Time Series Predictioncited by 4
  9. 2025A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMscited by 3
  10. 2026MODE: Efficient Time Series Prediction with Mamba Enhanced by Low-Rank Neural ODEscited by 1
  11. 2024Spatial-Temporal Generative AI for Traffic Flow Estimation with Sparse Data of Connected Vehiclescited by 0
  12. 2025GSTM-HMU: Generative Spatio-Temporal Modeling for Human Mobility Understandingcited by 0
2 more
  1. 2026UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integrationcited by 0
  2. 2026Efficient Prompt Learning for Traffic Forecastingcited by 0

Similar, not cited

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  1. 2024ProGen: Revisiting Probabilistic Spatial-Temporal Time Series Forecasting from a Continuous Generative Perspective Using Stochastic Differential Equations
  2. 2025Time Series Generation Under Data Scarcity: A Unified Generative Modeling Approach
  3. 2024Deep Temporal Deaggregation: Large-Scale Spatio-Temporal Generative Models
  4. 2024STD-PLM: Understanding Both Spatial and Temporal Properties of Spatial-Temporal Data with PLM
  5. 2026Spatial-Temporal State Propagation Autoregressive Model for 4D Object Generation
  6. 2024Diffusion Models as Data Mining Tools
  7. 2024STBench: Assessing the Ability of Large Language Models in Spatio-Temporal Analysis
  8. 2023Convolutional State Space Models for Long-Range Spatiotemporal Modeling