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Temporal data, notably time series and spatio-temporal data, are prevalent inreal-world applications. They capture dynamic system measurements and areproduced in vast quantities by both physical and virtual sensors. Analyzingthese data types is vital to harnessing the rich information they encompass andthus benefits a wide range of downstream tasks. Recent advances in largelanguage and other foundational models have spurred increased use of thesemodels in time series and spatio-temporal data mining. Such methodologies notonly enable enhanced pattern recognition and reasoning across diverse domainsbut also lay the groundwork for artificial general intelligence capable ofcomprehending and processing common temporal data. In this survey, we offer acomprehensive and up-to-date review of large models tailored (or adapted) fortime series and spatio-temporal data, spanning four key facets: data types,model categories, model scopes, and application areas/tasks. Our objective isto equip practitioners with the knowledge to develop applications and furtherresearch in this underexplored domain. We primarily categorize the existingliterature into two major clusters: large models for time series analysis(LM4TS) and spatio-temporal data mining (LM4STD). On this basis, we furtherclassify research based on model scopes (i.e., general vs. domain-specific) andapplication areas/tasks. We also provide a comprehensive collection ofpertinent resources, including datasets, model assets, and useful tools,categorized by mainstream applications. This survey coalesces the lateststrides in large model-centric research on time series and spatio-temporaldata, underscoring the solid foundations, current advances, practicalapplications, abundant resources, and future research opportunities.

Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook