Results 1 to 10 of about 3,246,403 (205)
Scalable spatiotemporal prediction with Bayesian neural fields. [PDF]
Nat CommunSpatiotemporal datasets, which consist of spatially-referenced time series, are ubiquitous in diverse applications, such as air pollution monitoring, disease tracking, and cloud-demand forecasting.
Saad F +6 more
europepmc +7 more sources
STMP-Net: A Spatiotemporal Prediction Network Integrating Motion Perception [PDF]
Sensors, 2023 This article proposes a video prediction network called STMP-Net that addresses the problem of the inability of Recurrent Neural Networks (RNNs) to fully extract spatiotemporal information and motion change features during video prediction.
Suting Chen, Ning Yang
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Spatiotemporal prediction of vancomycin-resistant Enterococcus colonisation. [PDF]
BMC Infect Dis, 2022 AbstractBackgroundVancomycin-resistant enterococci (VRE) is the cause of severe patient health and monetary burdens. Antibiotic use is a confounding effect to predict VRE in patients, but the antibiotic use of patients who may have frequented the same ward as the patient in question is often neglected.
van Niekerk JM +4 more
europepmc +7 more sources
Self-Attention ConvLSTM for Spatiotemporal Prediction
Proceedings of the AAAI Conference on Artificial Intelligence, 2020 Spatiotemporal prediction is challenging due to the complex dynamic motion and appearance changes. Existing work concentrates on embedding additional cells into the standard ConvLSTM to memorize spatial appearances during the prediction.
Zhihui Lin +4 more
semanticscholar +3 more sources
Advances in spatiotemporal graph neural network prediction research
International Journal of Digital Earth, 2023 Being a kind of non-Euclidean data, spatiotemporal graph data exists everywhere from traffic flow, air quality index to crime case, etc. Unlike the raster data, the irregular and disordered characteristics of spatiotemporal graph data have attracted the ...
Yi Wang
doaj +2 more sources
MSSTNet: A Multi-Scale Spatiotemporal Prediction Neural Network for Precipitation Nowcasting
Remote Sensing, 2022 Convolution-based recurrent neural networks and convolutional neural networks have been used extensively in spatiotemporal prediction. However, these methods tend to concentrate on fixed-scale spatiotemporal state transitions and disregard the complexity
Yuankang Ye +4 more
doaj +2 more sources
Prediction of Suspect Location Based on Spatiotemporal Semantics
ISPRS International Journal of Geo-Information, 2017 The prediction of suspect location enables proactive experiences for crime investigations and offers essential intelligence for crime prevention. However, existing studies have failed to capture the complex social location transition patterns of suspects
Lian Duan, Xinyue Ye, Tao Hu, Xinyan Zhu
doaj +3 more sources
STVMamba: precipitation nowcasting with spatiotemporal prediction model. [PDF]
Sci RepA lightweight rainfall nowcasting model is required by Sichuan provincial meteorological bureaus. Deep learning methods such as recurrent, convolutional, and Transformer models have been applied to precipitation prediction. However, recurrent models struggle with suboptimal parallel computational efficiency and error accumulation, convolutional models ...
Zou M, Wen L, Huang Y, He Y, Xiao J.
europepmc +3 more sources
A refined maximum predictability for next location prediction with fusion knowledge. [PDF]
PLoS ONEResearch on maximum predictability for next location prediction aims to derive the theoretical maximum accuracy that an ideal prediction model could achieve, which is crucial for analyzing travel regularity and evaluating prediction models.
Liuhong Huang +3 more
doaj +2 more sources
Spatiotemporal prediction of foot traffic [PDF]
Proceedings of the 5th ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising, 2021 Foot traffic is a business term to describe the number of customers that enter a point of interest (POI). This work aims to predict future foot traffic: the number of people from each census block group (CBG) that will visit each POI of a study region with potential applications in marketing and advertising.
Samiul Islam +7 more
openaire +1 more source