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Related Articles from SNS
From Coarse to Fine: Managing Temporal Granularity in Spatio-Temporal Data for Fine-Grained Traffic Prediction
Announce Type: new Abstract: Efficient acquisition, storage, and utilization of traffic data are critical challenges in spatio-temporal data management. Most traffic data systems collect and store observations at fixed, coarse-grained temporal intervals to reduce storage and computation costs. However, such coarse-grained data severely limits downstream applications that require predictions at a finer temporal granularity.
DRAN: A Distribution and Relation Adaptive Network for Spatio-temporal Forecasting
arXiv:2504.01531v4 Announce Type: replace Abstract: Accurate predictions of spatio-temporal systems are crucial for tasks such as system management, control, and crisis prevention. However, the inherent time variance of many spatio-temporal systems poses challenges to achieving accurate predictions whenever stationarity is not granted. In order to address non-stationarity, we propose a Distribution and Relation Adaptive Network (DRAN) capable of dynamically adapting to relation and...
ARMA approximation of a Non-separable Spatio-Temporal Model with Fractional Smoothnesses in Space and Time
arXiv:2604.26535v2 Announce Type: replace-cross Abstract: The Mat\'ern covariance model is ubiquitous in spatial modelling, but there is no default choice for spatio-temporal modelling. In this paper, we consider the recently proposed ``diffusion-based'' extension of the spatial Mat\'ern covariance model to a spatio-temporal non-separable covariance model that allows fractional smoothnesses in space and in time.
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
arXiv:2310.10196v3 Announce Type: replace Abstract: 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.
Knowledge-Preserved Model Tuning in Null-Space for Robust Spatio-Temporal Video Grounding
Announce Type: new Abstract: Spatio-Temporal Video Grounding aims to localize object tubes based on textual queries. While recent methods have achieved remarkable success, they mainly focus on high-quality(HQ) inputs, neglecting the widespread presence of low-quality(LQ) videos in real-world scenarios. Although tuning methods like LoRA can adapt to degraded inputs, they inevitably disrupt pre-trained knowledge.
GLIDE: Graph-guided Leap Inference for Diffusion Estimation of Spatio-Temporal Point Processes
arXiv:2606.01273v1 Announce Type: new Abstract: Spatio-temporal point processes (STPPs) provide a principled framework for modeling asynchronous events in continuous time and space. Recent diffusion-based approaches offer a flexible alternative to deterministic prediction by modeling complex conditional distributions, but their application to STPPs remains challenging: reverse sampling from pure noise is costly, and weak structural constraints in sparse spatial domains can lead to poorly...
Remember with Confidence: Uncertainty Quantification for Spatio-temporal Memory with Probabilistic Guarantees
Announce Type: new Abstract: Long-horizon robot operation requires spatio-temporal memory to record the environment state and recall it for downstream reasoning. Scene graphs and retrieval-augmented systems ground VLM descriptions to persistent 3D entities with rich semantic descriptions. However, VLM captions are noisy and viewpoint-inconsistent, and existing systems treat them as an oracle with no mechanism to detect unreliable stored descriptions.
Spatio-temporal stochastic graph-based learning for infectious disease forecasting
Announce Type: new Abstract: Spatio-temporal graph-based models have typically been used to forecast new cases of infectious diseases such as COVID-19 and chickenpox outbreaks. However, the use of stochastic modelling into their learning process has been surprisingly under-investigated and rarely considered entire data sets of large countries. As a result, it is unknown whether these models would provide accurate forecasts in real-world disease spread scenarios.
CausalPOI: Spatio-Temporal Graph-Based Causal Modeling for Cold-Start POI Check-in Forecasting
arXiv:2606.05413v1 Announce Type: new Abstract: As urban environments continue to evolve rapidly, accurately modeling the dynamic behaviour of Points of Interest is essential for supporting data-driven urban planning and commercial decision-making. While recent advancements in spatio-temporal graph learning have improved POI forecasting, most methods rely on proximity-based graphs and correlation-driven modeling, which overlook the functional dependencies between POIs and fail to capture the...
Disentangling spanwise asymmetries in unsteady wing wakes: global mode sensitivity and spatio-temporal harmonic resolvent analyses
arXiv:2606.00859v1 Announce Type: new Abstract: We investigate the emergence of long-time spanwise asymmetries in an unsteady wake downstream of a finite-span wing by disentangling flow asymmetries into symmetric and anti-symmetric components using global mode (structural) sensitivity and spatio-temporal harmonic resolvent analysis. The global mode sensitivity analysis shows that asymmetric modes emerge when symmetric and anti-symmetric eigenmodes appear as pairs and exhibit high levels of...