Survey of Large Models for Time Series and Spatio-Temporal Data Analysis
A comprehensive 35-page survey reviewing large language and foundation models applied to time series and spatio-temporal data analysis has been accepted by ACM Computing Surveys. The paper organizes existing research into two main groups—large models for time series analysis and for spatio-temporal data mining—covering data types, model categories, scopes, and application areas. It matters because it consolidates a rapidly growing field and identifies open research opportunities relevant to domains ranging from sensor networks to artificial general intelligence.
Authored by a large international team and accepted by ACM Computing Surveys, the survey provides a structured review of how large language models and other foundation models are being tailored or adapted for temporal data, including time series and spatio-temporal datasets generated by physical and virtual sensors. The authors organize the literature into two primary categories: large models for time series analysis (LM4TS) and large models for spatio-temporal data mining (LM4STD), with further distinctions between general-purpose and domain-specific models. The paper spans 35 pages and covers four analytical dimensions: data types, model categories, model scopes, and application areas and tasks. Alongside the conceptual review, the authors curate practical resources including datasets, model implementations, and tools organized by application domain. The survey argues that effective analysis of temporal data is critical for unlocking rich information content and advancing progress toward artificial general intelligence capable of understanding dynamic system behaviors. Originally posted to arXiv in October 2023, the paper reached its third version in June 2026, reflecting ongoing updates to keep pace with the fast-moving field.
What different sources said
- arXiv cs.AICenter
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
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