GDBs, unlike the traditional (relational) databases present in Information Health Systems, have a remarkable expressiveness for modeling and querying highly inter-liked concepts in data-sets. In particular, we focus on the study of the advantages GDBs can offer in the analysis of contacts between...
CIKM 2024于10月21号-10月25号在美国爱达荷州博伊西举行(Boise, Idaho, USA) 本文总结了CIKM 2024有关时空数据(spatial-temporal data)的相关论文,主要包含交通预测,插补,事故预测,气象预测,轨迹相似度计算,物流配送以及时空图神经网络在金融,供应链,能源等领域应用的相关内容,如有疏漏,欢迎大家补充。 Full Research...
Mobility-LLM: Learning Visiting Intentions and Travel Preference from Human Mobility Data with Large Language Models Addressing Spatial-Temporal Heterogeneity: General Mixed Time Series Analysis via Latent Continuity Recovery and Alignment Causal Deciphering and Inpainting in Spatio-Temporal Dynamics via Diffusi...
24 Ocean Significant Wave Height Estimation with Spatio-temporally Aware Large Language Models 25 Revealing the Power of Masked Autoencoders in Traffic Forecasting 26 Hol-Light: A Holistic framework for Efficient and Dynamic Traffic Signal Management 27 Empowering Traffic Speed Prediction with Auxiliary ...
1. Probabilistic Imputation for Time-series Classification with Missing Data 2. Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation 3. Deep Latent State Space Models for Time-Series Generation 4. Neural Stochastic Differential Games for Time-series Analysis ...
systematic models. 2.1 Acid rain in New York: Initial temporal analysis Among the earliest papers on spatial-temporal statistics were a series of papers by Bilonick and Nichols (1983) and Bilonick (1983, 1985, 1988). Bilonick and Nichols (1983) considered the analysis of rainfall data from ...
The model’s performance is rigorously evaluated when trained and applied with both synthetic and measured data, demonstrating superior accuracy and robustness in comparison to traditional numerical models in long-term forecasting. The study’s findings highlight the potential of ST-GNNs in environmental...
For example, player actions and tactical behaviours can be evaluated using changes in the expected point values during possessions, which are derived from stochastic process models that incorporate the spatial–temporal information of the players and the ball [12]. Additionally, machine learning ...
Spatial analysis of tuberculosis in an urban west African setting: is there evidence of clustering? Trop Med Int Health. 2010;15(6):664–72. Article CAS PubMed Google Scholar Wei X, Zou G, Yin J, Walley J, Sun Q. Comparing patient care seeking pathways in three models of hospital ...
3 Mobility-LLM: Learning Visiting Intentions and Travel Preference from Human Mobility Data with Large Language Models 4 Addressing Spatial-Temporal Heterogeneity: General Mixed Time Series Analysis via Latent Continuity Recovery and Alignment 5 Causal Deciphering and Inpainting in Spatio-Temporal Dynamics ...