Understanding high-dimensional dependencies in modern financial systems requires time series models that capture both contemporaneous and dynamic linkages. This study develops a sparse spatio-temporal vector autoregressive framework to analyse the ne...
Precise indoor thermal environment forecasting is critical for the energy-efficient operation and model predictive control of building heating, ventilation, and air-conditioning (HVAC) systems. However, conventional predictive models often struggle t...
Topology identification and signal inference are cornerstone tasks in graph signal processing (GSP). Structural Equation Modeling (SEM) is particularly effective for network inference as it explicitly captures causal dependencies. However, a major bo...
We developed a novel approach in the field of spatiotemporal modeling, based on the spatialisation of time, the Timescape algorithm. It is especially aimed at sparsely distributed datasets in ecological research, whose spatial and temporal variabilit...
The characteristics of multivariate heterogeneity in traffic flow forecasting exhibit significant variation, heavily influenced by spatio-temporal dynamics and unforeseen events. To address this challenge, we propose a spatio-temporal fusion graph ne...
The increasing discovery of underwater methane leakage underscores the importance of monitoring methane emissions for environmental protection. Underwater remote sensing of methane leakage is critical and meaningful to protect the environment. The co...
Crime prediction is crucial for sustainable urban development and protecting citizens’ quality of life. However, there exist some challenges in this regard. First, the spatio-temporal correlations in crime data are relatively complex and are he...
The modeling, computational complexity, and accuracy of spatio-temporal models are the three major foci in the field of video action recognition. The traditional 2D convolution has low computational complexity, but it cannot capture the temporal rela...
In response to the redundant spatio-temporal modeling and insufficient adaptation to dynamic decision-making in eye-tracking interaction of smart home interfaces, a smart home interface eye-tracking response optimization model based on spatio-tempora...
The integration of heterogeneous spatio-temporal datasets presents a critical challenge in geospatial data science, particularly when combining large-scale, passively collected “big” data with precise but sparse “small” data....
Weather radar, as a crucial component of remote sensing data, plays a vital role in convective weather forecasting through radar echo extrapolation techniques. To address the limitations of existing deep learning methods in radar echo extrapolation,...
Human motion prediction is critical for applications such as autonomous driving and healthcare. However, existing methods struggle to balance diversity (varied motion patterns), fidelity (realistic and accurate motion), and computational efficiency....
Point-of-Interest (POI) recommendation predicts users’ future check-ins based on their historical trajectories and plays a key role in location-based services (LBS). Traditional approaches such as collaborative filtering and matrix factorizatio...
Information is the fundamental currency of naturally occurring complex adaptive systems, whether they are individual organisms or collective social insect colonies. Information appears to be more important than energy in determining the behavior of t...
Infrared small target detection (ISTD) has been a critical technology in various civilian and industrial applications over the past several decades, such as civilian patrol missions aboard UAVs or shipboard systems, and industrial inspection tasks li...
In the context of global warming, the accurate prediction of Arctic Sea Ice Concentration (SIC) is crucial for the development of Arctic shipping routes. We have therefore constructed a lightweight, non-recursive spatio-temporal prediction model, the...
In urban environmental management and public health evaluation efforts, there is an urgent need for fine-grained urban air quality monitoring. However, the high price and sparse distribution of air quality monitoring equipment make it difficult to de...
In this paper, a novel infrared target co-detection model combining the self-correlation features of backgrounds and the commonality features of targets in the spatio-temporal domain is proposed to detect small targets in a sequence of infrared image...
Video scene graph generation (ViDSGG), the creation of video scene graphs that helps in deeper and better visual scene understanding, is a challenging task. Segment-based and sliding-window based methods have been proposed to perform this task. Howev...
Unmanned Aerial Vehicle (UAV) visual tracking is widely used but continues to face challenges such as unpredictable target motion, error accumulation, and the sparse appearance of small targets. To address these issues, we propose a Mamba-based Spati...
Accurate forecasting of photovoltaic (PV) power is crucial for real-time grid balancing and storage optimization. However, the intermittent, noisy, and nonstationary nature of PV generation, together with cross-site interactions, makes multi-site int...
Modeling the complex spatio-temporal dynamics of infectious diseases presents a significant computational challenge due to heterogeneous regional interactions, high-dimensional multimodal data streams, and the critical need for algorithmic fairness....
High-precision wind power prediction improves grid stability and reduces curtailment losses. Existing methods face three limitations: static graphs cannot capture dynamic spatial correlations under weather changes, time series models miss multi-scale...
Ground surface subsidence resulting from underground mining is characterized by significant spatial deformation gradients and nonlinear temporal deformation. A single monitoring technique is insufficient to simultaneously achieve extensive spatial co...
Industrial control systems (ICSs) face escalating security challenges due to evolving cyber threats and the inherent limitations of traditional intrusion detection methods, which fail to adequately model spatiotemporal dependencies or interpret compl...
Compressed sensing (CS) and matrix completion algorithms (MCA) have each introduced sparse and low-rank priors into synthetic aperture radar (SAR) imaging. However, their combined use reveals a fundamental zero-sum trade-off: enhancing spatial contin...
Fluid mechanics often involves complex systems characterized by a large number of physical parameters, which are usually described by experimental and numerical sparse data (temporal or spatial). The difficulty of obtaining complete spatio-temporal d...
Spiking neural networks (SNNs) have emerged as a promising model for energy-efficient, event-driven processing of asynchronous event streams from Dynamic Vision Sensors (DVSs), a class of neuromorphic image sensors with microsecond-level latency and...
Aiming at the problem that the existing human skeleton behavior recognition methods are insensitive to human local movements and show inaccurate recognition in distinguishing similar behaviors, a multi-scale spatio-temporal graph convolution method i...
The Received Signal Strength (RSS) fingerprint-based indoor localization is an important research topic in wireless network communications. Most current RSS fingerprint-based indoor localization methods do not explore and utilize the spatial or tempo...
Existing point-of-interest (POI) recommendation methods often fail to capture complex contextual dependencies and suffer from severe data sparsity in location-based social networks (LBSNs). To address these limitations, this study proposes a Context-...
Chlorophyll-a concentration (Chl-a) is a key indicator of marine water quality, phytoplankton biomass, and aquatic ecosystem status. Multi-step forecasting is challenging because Chl-a dynamics exhibit regional heterogeneity, multi-scale variability,...
Traffic flow forecasting provides essential support for the construction of smart transportation systems. Despite the superiority of the ASTGCN, which uses an attention mechanism to capture spatio-temporal correlations, it lacks an explicit physical...
Transformer-based methods have recently demonstrated their potential in time series forecasting problems. However, the mainstream approach, primarily utilizing attention to model inter-step correlation in the time domain, is constrained by two signif...
The article presents a new space-time adaptive processing (STAP) method for target detection in a heterogeneous and non-stationary environment. In study it was proven that it is possible to estimate the clutter covariance matrix (CCM) in STAP by usin...
High-precision traffic flow prediction facilitates intelligent traffic control and refined management decisions. Previous research has built a variety of exquisite models with good prediction results. However, they ignore the reality that traffic flo...
Reliable environmental perception capabilities are a prerequisite for achieving autonomous driving. Cameras and LiDAR are sensitive to illumination and weather conditions, while millimeter-wave radar avoids these issues. Existing models rely heavily...
Conventional approaches to video action recognition perform global attention over the entire video patches, which may be ineffective due to the temporal redundancy of video frames. Recent works on masked video modeling adopt a high-ratio tube masking...
Reliable air-quality monitoring is essential for urban exposure assessment and environmental policy, yet many downstream applications are hindered by sparse regulatory stations and noisy real-world measurements. While diffusion models have shown prom...
Real surveillance systems require weakly supervised video anomaly detection due to the fact that long untrimmed videos do not always have accurate temporal labels. Models will be required to label a video as normal or abnormal and also to identify sp...
Semi-arid loess basins in the middle Yellow River have rapid runoff responses, short hydrological memory, and sparse gauging networks, so pooled metrics across all stations can conceal forecast failures in low-flow tributaries and during dry periods....
Reconstructing and predicting multivariate environmental fields requires models that propagate information coherently across variables, space, and time, while allowing cross-dependence to change. We propose a Bayesian dynamic mixture of conditional G...
Modeling and forecasting online illegal transactions require representations of recurrent temporal variation, uneven spatial activity, and changing local conditions. This paper studies a framework consisting of spatio-temporal assessment, adaptive pe...
The stock market is of paramount importance to economic development. Investors who accurately predict stock price fluctuations based on its high volatility can effectively mitigate investment risks and achieve higher returns. Traditional time series...
To tackle the challenge of enhancing moving target detection performance in environments characterized by small sample sizes and non-uniformity, methods rooted in sparse signal reconstruction have been incorporated into Space-Time Adaptive Processing...
Background/Objectives: Wearable affective human–computer interaction increasingly relies on sparse-channel EEG signals to ensure comfort and practicality in real-life scenarios. However, the limited information provided by sparse-channel EEG, t...
Sleep quality is heavily influenced by sleep posture, with research indicating that a supine posture can worsen obstructive sleep apnea (OSA) while lateral postures promote better sleep. For patients confined to beds, regular changes in posture are c...
Electric vehicle (EV) charging behavior exhibits strong spatio-temporal randomness, often leading to transient peak loads and an elevated risk of distribution network overloads. In addition, existing prediction models still face challenges in achievi...
Many devices have been used to detect human action, including wearable devices, cameras, lidars, and radars. However, some people, such as the elderly and young children, may not know how to use wearable devices effectively. Cameras have the disadvan...
Operational weather satellites, dating back to 1970s, currently provide the best basis for climatological investigations, such as an analysis of changes in the cloud cover. Because clouds are highly dynamic in time, temporally high-resolution data fr...
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