19th International Symposium on Spatial and Temporal Data

Spatial regionalization seeks to partition a set of spatial polygons into contiguous, non-overlapping regions that optimize a specified objective function. This problem is fundamental in diverse applications across environmental science, urban planning, and public health, serving purposes such as resource allocation, policy formulation, and disease monitoring. Due to its computational complexity as an NP-hard problem, researchers typically rely on heuristics and approximation algorithms to achieve a practical balance between accuracy and run-time efficiency. This tutorial comprehensively reviews the main methodologies in the field, systematically categorizing them into three primary groups: (i) linear and integer-programming formulations, (ii) top-down divisive strategies, and (iii) bottom-up agglomerative techniques. We detail each category by highlighting the core principles and representative algorithms. Furthermore, we identify and discuss open challenges in spatial regionalization.

Multivariate time series (MTS) data are ubiquitous in complex dynamic systems such as meteorology, transportation, and energy. However, data heterogeneity caused by cross-domain variations has become a central bottleneck restricting model generalization and consistency in comparative studies. This paper systematically reviews recent MTS forecasting research, revealing that inconsistencies in experimental conclusions primarily arise from neglecting substantial differences in data distributions and characteristics. To address this issue, we introduce BasicTS, an fair and scalable benchmark designed to fairly quantify the impact of heterogeneity on model performance. Subsequently, to tackle generalization challenges posed by heterogeneity, this tutorial proposes two adaptive solutions: (i) developing BLAST, a balanced and diversity-enhanced pre-training corpus that explicitly models heterogeneity, significantly improving zero-shot general forecasting; and (ii) introducing ARIES, a relational assessment and model recommendation framework that leverages a statistical pattern-to-model matching mechanism to automatically select optimal forecasting models for specific real-world sequences. Through comprehensive experiments and case studies, we demonstrate that precisely characterizing and leveraging data heterogeneity, beyond mere model design, is crucial for improving the robustness of MTS forecasting. This research provides methodological guidance and practical insights for academia and industry to fully exploit the value of time series data and make data-driven decisions.

Large Language Models (LLMs) have emerged as a promising paradigm for time series analytics, leveraging their massive parameters and the shared sequential nature of textual and time series data. However, a cross-modality gap exists between time series and textual data, as LLMs are pre-trained on textual corpora and are not inherently optimized for time series. In this tutorial, we provide an up-to-date overview of LLM-based cross-modal time series analytics. We introduce a taxonomy that classifies existing approaches into three groups based on cross-modal modeling strategies, e.g., conversion, alignment, and fusion, and then discuss their applications across a range of downstream tasks. In addition, we summarize several open challenges. This tutorial aims to expand the practical application of LLMs in solving real-world problems in cross-modal time series analytics while balancing effectiveness and efficiency. Participants will gain a thorough understanding of current advancements, methodologies, and future research directions in cross-modal time series analytics.

Spatio-temporal data are foundational to understanding and modeling dynamic real-world phenomena such as human mobility, traffic flow, epidemic spread, and urban dynamics. With the growing availability of location-aware web data and the rise of intelligent urban infrastructures, analyzing spatio-temporal patterns has become both highly valuable and technically challenging. This tutorial provides a comprehensive overview of spatio-temporal data analytics, unifying perspectives from data management, machine learning, and emerging foundation models. We begin with a review of spatio-temporal data management systems, introducing the core data models, spatial-temporal indexing techniques, and scalable architectures for storing and querying large-scale mobility data. We then delve into trajectory learning, covering methods for prediction, generation, and reconstruction of movement sequences at the individual level. Next, we explore spatio-temporal graph learning, which focuses on forecasting region-level dynamics using dynamic graph neural networks. Multi-region, multi-task, and multi-domain spatio-temporal learning will be identified and introduced in detail. Finally, we present advanced learning frameworks that integrate federated learning, continual learning, and LLM-based approaches to build scalable, adaptive, and privacy-preserving spatio-temporal models. Through the lens of recent methodological and system-level advances, this tutorial bridges algorithmic design and practical deployment of spatio-temporal learning systems. It is suitable for researchers and practitioners working in machine learning, data mining, geospatial analysis, and intelligent systems.

Spatiotemporal data modeling has long been a fundamental task across disciplines such as environmental science, transportation, and climate analytics. A typical goal is to estimate unknown information at specific spatiotemporal points based on partially observed data\—--for example, interpolating weather conditions at unmeasured locations, reconstructing missing historical records, or forecasting the future trajectories of financial markets. These are all core tasks within the broader scope of spatiotemporal modeling. This tutorial (1 hours) introduces a cohesive view of spatiotemporal data modeling, tracing the evolution from traditional statistical approaches to modern deep learning paradigms. We begin by revisiting Kriging and time series decomposition to highlight the essential assumptions and strengths of these classical methods. Next, we explore low-rank matrix and tensor completion techniques, which leverage the structured patterns of spatiotemporal data. We then transition to spatiotemporal graph neural networks, which model complex dependencies by integrating graph structures with dynamic temporal features. Finally, we discuss recent advances in applying large foundation models to spatiotemporal tasks, including their capabilities and current limitations. Throughout the tutorial, we emphasize how lessons from traditional methods—such as the importance of locality, periodicity, and smoothness priors—can inspire new directions for developing and fine-tuning foundation models in the spatiotemporal domain. We conclude by outlining key challenges and opportunities in bridging classical wisdom with emerging AI capabilities.