Forecasting pedestrian congestion in urban back streets is challenging due to “shadow areas” where CCTV coverage is absent and trajectory data cannot be directly collected. To address these gaps, we propose the Peak-aware Graph-attention...
Photovoltaic (PV) power interval forecasting models are commonly trained on data dominated by non-ramp samples, which may weaken uncertainty characterization during rapid power changes. This study proposes a ramp-aware quantile regression Temporal Fu...
Misinformation propagates more rapidly than factual content on social media, presenting significant challenges for automated misinformation detection. Existing approaches often focus solely on textual features without incorporating temporal informati...
The energy generated by a solar photovoltaic (PV) system depends on uncontrollable factors, including weather conditions and solar irradiation, which leads to uncertainty in the power output. Forecast PV power generation is vital to improve grid stab...
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....
Accurate short-term electric load forecasting is essential for the operation and management of energy-intensive manufacturing processes such as quicklime production, for which power demand is driven by stage-based operation, fixed schedules, and abru...
Hot dry rock (HDR) represents a highly promising sustainable clean energy resource for the future, with the fracture network induced by hydraulic fracturing serving as the cornerstone for thermal energy extraction efficiency. However, conventional nu...
2 May 2025
As globalization deepens and the digital economy rapidly develops, cross-border e-commerce, especially live-streaming e-commerce, has emerged as a significant driver of international trade growth. However, the highly unpredictable sales demand in thi...
Forecasting stock prices remains a central challenge in financial modelling, as markets are influenced by market sentiment, firm-level fundamentals and complex interactions between macroeconomic and microeconomic factors, for example. This study eval...
Accurate load loss forecasting is important to prevent plant failures and improve power station reliability. While the Temporal Fusion Transformer and LSTM have demonstrated state-of-the-art performance in modelling complex temporal patterns, their e...
Accurate reservoir water level forecasting is essential for water resource management, flood risk mitigation, and hydropower operation. However, it remains challenging due to pronounced geographical heterogeneity and complex multi-scale temporal dyna...
Solar power is a clean and sustainable energy source that does not emit greenhouse gases or other atmospheric pollutants. The inherent variability in solar energy due to random fluctuations introduces novel attributes to the power generation and load...
This study employs a temporal fusion transformer (TFT) for predicting overflow from sewer manholes during heavy rainfall events. The TFT utilised is capable of forecasting overflow hydrographs at the manhole level and was tested on a sewer network wi...
Recently, studies on driver inattention state recognition as an advanced mobility application technology are being actively conducted to prevent traffic accidents caused by driver drowsiness and distraction. The driver inattention state recognition s...
Accurate prediction of the Sharpe ratio, a key metric for risk-adjusted returns in financial markets, remains a significant challenge due to the complex and stochastic nature of stock price movements. This paper introduces a novel deep learning model...
Short-term load forecasting plays a crucial role in managing the energy consumption of buildings in cities. Accurate forecasting enables residents to reduce energy waste and facilitates timely decision-making for power companies’ energy managem...
To address the challenges brought by the high penetration of renewable energy in power systems, such as multi-scale dynamic interactions, high feature dimensionality, and limited model generalization, this paper proposes a transient stability assessm...
Accurate forecasting of battery state of health (SOH), wind power, and photovoltaic (PV) power is challenged by non-stationarity, multiscale dependencies, and physically implausible outputs. This study proposes a physically constrained Temporal Fusio...
Bike-sharing systems (BSS) have emerged as an increasingly important form of transportation in smart cities, playing a pivotal role in the evolving landscape of urban mobility. As cities worldwide strive to promote sustainable and efficient transport...
Cryptocurrency markets are characterized by high volatility, nonlinear dependencies, and limited transparency, making short-term forecasting particularly challenging for both researchers and practitioners. To address these complexities, this study in...
Accurate one-week-ahead building electricity demand forecasting is essential for building energy management, yet representing future building operational characteristics remains challenging because such information is generally unavailable in advance...
Accurate traffic prediction is crucial for optimizing taxi demand, managing traffic flow, and planning public transportation routes. Traditional models often fail to capture complex spatial–temporal dependencies. To tackle this, we introduce th...
The operational integration of renewable energy into contemporary power systems requires accurate and dependable wind power forecasting, particularly in multi-site settings with nonlinear temporal dynamics, inter-site dependence, and forecast uncerta...
This study develops an interpretable, data-driven framework for forecasting daily MDA8 ozone levels in the Beijing–Tianjin–Hebei (BTH) region, integrating statistical diagnostics, XGBoost-based SHAP feature interpretation, and the Tempora...
This study explores the application of Temporal Fusion Transformers (TFTs) to improve the predictability of hourly potential hydropower production for a small run–of–the–river hydropower plant in Portugal. Accurate hourly power fore...
A key element for reducing energy consumption and improving thermal comfort on high-speed rail is controlling air-conditioning temperature. Accurate prediction of air supply temperature is aimed at improving control effects. Existing studies of suppl...
Traffic flow prediction is essential for smart city management and planning, aiding in optimizing traffic scheduling and improving overall traffic conditions. However, due to the correlation and heterogeneity of traffic data, effectively integrating...
In the realm of Intelligent Transportation Systems (ITSs), traffic flow prediction is crucial for multiple applications. The primary challenge in traffic flow prediction lies in the handling and modeling of the intricate spatial–temporal correl...
Anomaly detection tasks involving time-series signal processing have been important research topics for decades. In many real-world anomaly detection applications, no specific distributions fit the data, and the characteristics of anomalies are diffe...
Rising renewable penetration increases wholesale price volatility and challenges traditional forecasting approaches. This paper benchmarks three methods—adaptive linear regression, Random Forest (RF), and the Temporal Fusion Transformer (TFT)&m...
Single-source remote sensing image (RSI) cannot simultaneously meet high-spatial and high-temporal resolution requirements, failing to provide decision-makers with timely and accurate monitoring data. Spatiotemporal fusion (STF) of multi-source RSIs...
Deep neural network fusion approaches utilizing multimodal remote sensing are essential for crop mapping. However, challenges such as insufficient spatiotemporal feature extraction and ineffective fusion strategies still exist, leading to a decrease...
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....
The decoding of motor imagery (MI) electroencephalogram (EEG) signals is crucial for motor control and rehabilitation. However, as feature extraction is the core component of the decoding process, traditional methods, often limited to single-feature...
The actuator plays a crucial role in controlling the flight attitude of unmanned aerial vehicles (UAVs), making timely anomaly detection essential for operational reliability and flight safety. However, existing methods often have difficulty jointly...
Improving the accuracy of the forecasting of building power consumption is helpful in reducing commercial expenses and carbon emissions. However, challenges such as the shortage of training data and the absence of efficient models are the main obstac...
More than 14% of the world’s population suffered from diabetes mellitus in 2022. This metabolic condition is defined by increased blood glucose concentrations. Among the different types of diabetes, type 1 diabetes, caused by a lack of insulin...
The accurate prediction of carbon dioxide (CO2) emissions from light-duty vehicles is critical for mitigating environmental impacts and enhancing regulatory compliance in the automotive industry. However, challenges such as high-dimensional feature s...
Capturing complex temporal dependencies across multiple scales remains a fundamental challenge in time series forecasting. Transformer-based models have achieved impressive performance on sequence tasks, but vanilla designs often struggle to integrat...
The increasing share of renewable energies within energy systems leads to an increase in complexity. The growing complexity is due to the diversity of technologies, ongoing technological innovations, and fluctuating electricity production. To continu...
Accurately predicting the remaining useful life (RUL) is crucial for ensuring the safety and reliability of aircraft engine operation. However, aircraft engines operate in harsh conditions, with the characteristics of high speed, high temperature, an...
Multimodal emotion recognition is challenging because behavioral signals and physiological responses evolve at different temporal rates. Facial expressions and speech often change rapidly after an emotional event, whereas peripheral biosignals such a...
Remote sensing products with high temporal and spatial resolution can be hardly obtained under the constrains of existing technology and cost. Therefore, the spatiotemporal fusion of remote sensing images has attracted considerable attention. Spatiot...
High-resolution remote sensing imagery encompasses complex land cover types and rich textural details, whilst temporal variations often manifest as subtle feature differences and unstable structural patterns. This renders traditional change detection...
5 January 2026
Vector-borne diseases are strongly influenced by climate, yet the magnitude and temporal variability of climate–disease relationships remain poorly quantified. Outbreaks occur abruptly, and responses are typically delayed, underscoring the need...
Egocentric hand gesture recognition is vital for natural human–computer interaction in augmented and virtual reality (AR/VR) systems. However, most deep learning models struggle to balance accuracy and efficiency, limiting real-time use on wear...
This study introduces a unified and methodologically symmetric comparative framework for multivariate cryptocurrency forecasting, addressing long-standing inconsistencies in prior research where model families, feature sets, and preprocessing pipelin...
Remote sensing change detection (RSCD) has advanced through convolutional, attention-based, transformer, and hybrid architectures, yet models are commonly compared as whole architectural families rather than by how their temporal streams interact. Th...
NFT prices are shaped by heterogeneous signals including visual appearance, textual narratives, transaction trajectories, and on-chain interactions, yet existing studies often model these factors in isolation and rarely unify multimodal alignment, te...
In order to improve the prediction performance of existing methods amidst multi modulation coupling interference in complex electromagnetic environments, this paper introduces a novel approach that integrates wavelet transform with a temporal convolu...
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