arXiv:2607. 20592v1 Announce Type: new Abstract: Spatio-temporal machine-learning modelling is an important tool in environmental research.
By Jan Linnenbrink, Jakub Nowosad, Marvin Ludwig, Anna Frederike Jablotschkin, Fabian Schumacher, Teja Kattenborn, Hanna Meyer
arXiv:2601. 11046v2 Announce Type: replace Abstract: Machine learning models for climate and Earth science are becoming increasingly capable, yet model deployment into operational use remains a largely unaddressed challenge: general-purpose model-serving tools, such as MLflow and KServe, assume input data availability at the inference node, while data acquisition, failure handling, and preprocessing are trusted to a separate workflow.
By Shahbaz Alvi, Giusy Fedele, Gabriele Accarino, Italo Epicoco, Ilenia Manco, Pasquale Schiano
arXiv:2607. 01022v1 Announce Type: new Abstract: Spatiotemporal point processes (STPPs) model event data in continuous time and space, with applications in mobility, epidemiology, and public safety.
By Yahya Aalaila, Gerrit Gro{\ss}mann, Sebastian Vollmer
arXiv:2310. 10196v3 Announce Type: replace-cross Abstract: Temporal data, including time series and spatio-temporal data, are pervasive in real-world applications.
By Ming Jin, Yaxuan Kong, Yuxuan Liang, Chaoli Zhang, Siqiao Xue, Xue Wang, James Zhang, Yi Wang, Haifeng Chen, Xiaoli Li, Vincent S. Tseng, Yu Zheng, Lei Chen, Hui Xiong, Shirui Pan, Qingsong Wen
arXiv:2607. 24218v1 Announce Type: cross Abstract: Federated learning has emerged as a promising paradigm for spatio-temporal forecasting (STF), enabling collaborative model training without sharing raw observations.
By Qingxiang Liu, Anqi Liang, Heng Wang, Yuxuan Liang
arXiv:2606. 05413v1 Announce Type: new Abstract: As urban environments continue to evolve rapidly, accurately modeling the dynamic behaviour of Points of Interest is essential for supporting data-driven urban planning and commercial decision-making.
By Zhaoqi Zhang, Miao Xie, Yi Li, Linyou Cai, Siqiang Luo, Gao Cong