Zobrazeno 1 - 10
of 30 234
pro vyhledávání: '"spatio temporal data"'
Many important phenomena in scientific fields such as climate, neuroscience, and epidemiology are naturally represented as spatiotemporal gridded data with complex interactions. For example, in climate science, researchers aim to uncover how large-sc
Externí odkaz:
http://arxiv.org/abs/2411.05331
Autor:
Fournier, Claudia, Fernandez-Fernandez, Raul, Cirés, Samuel, López-Orozco, José A., Besada-Portas, Eva, Quesada, Antonio
Publikováno v:
Water Research (2024) Volume 267 ISSN: 0043-1354
Cyanobacteria are the most frequent dominant species of algal blooms in inland waters, threatening ecosystem function and water quality, especially when toxin-producing strains predominate. Enhanced by anthropogenic activities and global warming, cya
Externí odkaz:
http://arxiv.org/abs/2410.08237
Publikováno v:
Neural Networks 2025, 181, 106774
Modelling multivariate spatio-temporal data with complex dependency structures is a challenging task but can be simplified by assuming that the original variables are generated from independent latent components. If these components are found, they c
Externí odkaz:
http://arxiv.org/abs/2409.04162
Accurately forecasting dynamic processes on graphs, such as traffic flow or disease spread, remains a challenge. While Graph Neural Networks (GNNs) excel at modeling and forecasting spatio-temporal data, they often lack the ability to directly incorp
Externí odkaz:
http://arxiv.org/abs/2408.16379
Many important problems require modelling large-scale spatio-temporal datasets, with one prevalent example being weather forecasting. Recently, transformer-based approaches have shown great promise in a range of weather forecasting problems. However,
Externí odkaz:
http://arxiv.org/abs/2410.06731
The event camera has demonstrated significant success across a wide range of areas due to its low time latency and high dynamic range. However, the community faces challenges such as data deficiency and limited diversity, often resulting in over-fitt
Externí odkaz:
http://arxiv.org/abs/2409.11813
In the AIOps (Artificial Intelligence for IT Operations) era, accurately forecasting system states is crucial. In microservices systems, this task encounters the challenge of dynamic and complex spatio-temporal relationships among microservice instan
Externí odkaz:
http://arxiv.org/abs/2408.07894