Yu Zheng

Temporal data — including time series and spatio-temporal data — are pervasive in real-world applications. Generated in massive volumes by physical and virtual sensors, they record dynamic system behaviors and enable a wide range of downstream tasks. Effectively analyzing such data is crucial to unlocking their rich information content. Recent advances in large language models and other foundation models have accelerated their use in time series and spatio-temporal data mining. These approaches not only improve pattern recognition and reasoning across diverse domains but also support progress toward artificial general intelligence that can understand and process temporal data. In this survey, we present a comprehensive, up-to-date review of large models tailored or adapted for time series and spatio-temporal data along four dimensions: data types, model categories, model scopes, and application areas/tasks. We organize existing work into two main groups: large models for time series analysis (LM4TS) and for spatio-temporal data mining (LM4STD), and further distinguish general-purpose from domain-specific models. We also curate related resources, including datasets, model implementations, and tools, organized by major application areas. Overall, this survey consolidates recent advances and highlights foundations, applications, resources, and open research opportunities in large model–centric temporal data analysis.

Urban sensing is a critical infrastructure for smart cities, yet dense sensor deployments remain prohibitively costly, and sensor data quality degrades severely under real-world interference (e.g., signal occlusion, extreme weather) that results in data gaps. Existing approaches treat sampling and imputation as two independent stages: sampling strategies select locations based on heuristics or optimization criteria without considering the downstream reconstruction objective, while imputation models passively adapt to whatever observations are provided, with no mechanism to inform the sampler which locations would most effectively reduce reconstruction error. We propose ST-CoSense, a unified framework that bridges this gap through end-to-end joint optimization of active sparse sampling and spatio-temporal imputation. A mask-guided encoder dynamically selects informative sensing locations by integrating historical sampling patterns with learned spatio-temporal representations, while an attention-based imputation module reconstructs complete urban data from sparse observations. Imputed results in turn refine subsequent sampling decisions, allowing continuous adaptation under sampling budget constraints. Experimental results show that ST-CoSense outperforms state-of-the-art methods, achieving up to 9.07% MAE and 10.53% RMSE improvement at a 50% sampling rate. Furthermore, ST-CoSense demonstrates superior robustness in large-scale urban networks, achieving a 4.95% RMSE improvement over the best-performing baseline. These results underscore ST-CoSense's effectiveness and its practical potential in resource-constrained urban sensing applications.

Urban drainage systems, often designed for out dated rainfall assumptions, are increasingly unable to cope with extreme rainfall events. This leads to flooding, infrastructure damage, and economic losses, necessitating effective diagnostic and improvement strategies. In current practice, conventional analysis platforms built on hydrological-hydraulic models provide only limited analytical support, making it difficult to pin point defects, inspect causal mechanisms, or evaluate alternative design options in an integrated manner. In this paper, we develop DrainScope, to our knowledge, the first visual analytics approach for comprehensive diagnosis and iterative improvement of urban drainage systems. Defects are initially observed in the map view, after which DrainScope extracts the critical sub-systems associated with them using a rule-based search strategy, enabling focused analysis. It introduces a novel drainage-oriented Sankey diagram to visualize internal flow dynamics within the focused, static drainage system, revealing the causes of identified system defects. Furthermore, it enables flexible modification of drainage components corresponding to identified defects, coupled with a comparison view for rapid, iterative evaluation of improvement plans. We evaluate DrainScope through a real-world case study and positive feedback collected from domain experts.