7 papers
A Critical Audit of Spatiotemporal Forecasting Benchmark Datasets and Baselines
Kenneth Martin, Simon Heilig, Asja Fischer +3
Graph neural networks (GNNs) are routinely employed for short-range forecasting on multivariate time series with a spatial graph structure. Despite the availability of many alterna…
The Whittle likelihood for mixed models with application to groundwater level time series
Jakub J. Pypkowski, Adam M. Sykulski, James S. Martin +1
Understanding the processes that influence groundwater levels is crucial for forecasting and responding to hazards such as groundwater droughts. Mixed models, which combine a fixed…
Causal tail coefficient for compound extremes in multivariate time series
Cathy Yin, Adam M. Sykulski, Almut E. D. Veraart
Extreme events are often multivariate in nature. A compound extreme occurs when a combination of variables jointly produces a significant impact, even if individual components are…
Navigating Challenges in Spatio-temporal Modelling of Antarctic Krill Abundance: Addressing Zero-inflated Data and Misaligned Covariates
André Victor Ribeiro Amaral, Adam M. Sykulski, Sophie Fielding +1
Antarctic krill (Euphausia superba) are among the most abundant species on our planet and serve as a vital food source for many marine predators in the Southern Ocean. In this pape…
SplitWise Regression: Stepwise Modeling with Adaptive Dummy Encoding
Marcell T. Kurbucz, Nikolaos Tzivanakis, Nilufer Sari Aslam +1
Capturing nonlinear relationships without sacrificing interpretability remains a persistent challenge in regression modeling. We introduce SplitWise, a novel framework that enhance…
Isotropy testing in spatial point patterns: nonparametric versus parametric replication under misspecification
Jakub J. Pypkowski, Adam M. Sykulski, James S. Martin
Several hypothesis testing methods have been proposed to validate the assumption of isotropy in spatial point patterns. A majority of these methods are characterised by an unknown…