67 citations · 194 across the 24 of their papers we have counts for
30 papers
Statistical Oceanography of Profiling Floats and Surface Drifters
Mikael Kuusela, Sofia C. Olhede, Adam M. Sykulski
The Global Ocean Observing System is key to understanding oceanic variability and climate change. The ocean is vast and often sparsely and irregularly sampled in time and space by…
A Critical Audit of Spatiotemporal Forecasting Benchmark Datasets and Models
Kenneth Martin, Simon Heilig, Asja Fischer +3
Graph neural networks (GNNs) are routinely employed for spatiotemporal forecasting, yet their performance across widely used benchmark datasets is inconsistent. Here, we perform an…
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…
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…
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…
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…
Bias correction of quadratic spectral estimators
Lachlan Astfalck, Adam Sykulski, Edward Cripps
The three cardinal, statistically consistent, families of non-parametric estimators to the power spectral density of a time series are lag-window, multitaper and Welch estimators.…
Debiasing Welch's Method for Spectral Density Estimation
Lachlan C. Astfalck, Adam M. Sykulski, Edward J. Cripps
Welch's method provides an estimator of the power spectral density that is statistically consistent. This is achieved by averaging over periodograms calculated from overlapping seg…
Analysing and visualising bike-sharing demand with outliers
Nicola Rennie, Catherine Cleophas, Adam M. Sykulski +1
Bike-sharing is a popular component of sustainable urban mobility. It requires anticipatory planning, e.g. of station locations and inventory, to balance expected demand and capaci…
A multivariate pseudo-likelihood approach to estimating directional ocean wave models
Jake P. Grainger, Adam M. Sykulski, Kevin Ewans +2
Ocean buoy data in the form of high frequency multivariate time series are routinely recorded at many locations in the world's oceans. Such data can be used to characterise the oce…
A Dataset of Hourly Sea Surface Temperature From Drifting Buoys
Shane Elipot, Adam Sykulski, Rick Lumpkin +2
A dataset of sea surface temperature (SST) estimates is generated from the temperature observations of surface drifting buoys of NOAA's Global Drifter Program. Estimates of SST at…
Multivariate Probabilistic Regression with Natural Gradient Boosting
Michael O'Malley, Adam M. Sykulski, Rick Lumpkin +1
Many single-target regression problems require estimates of uncertainty along with the point predictions. Probabilistic regression algorithms are well-suited for these tasks. Howev…
Outlier detection in network revenue management
Nicola Rennie, Catherine Cleophas, Adam M. Sykulski +1
This paper presents an automated approach for providing ranked lists of outliers in observed demand to support analysts in network revenue management. Such network revenue manageme…
Separating Mesoscale and Submesoscale Flows from Clustered Drifter Trajectories
Sarah Oscroft, Adam M. Sykulski, Jeffrey J. Early
Drifters deployed in close proximity collectively provide a unique observational data set with which to separate mesoscale and submesoscale flows. In this paper we provide a princi…
Estimating the parameters of ocean wave spectra
Jake P. Grainger, Adam M. Sykulski, Philip Jonathan +1
Wind-generated waves are often treated as stochastic processes. There is particular interest in their spectral density functions, which are often expressed in some parametric form.…
Nonparametric Time Series Summary Statistics for High-Frequency Accelerometry Data from Individuals with Advanced Dementia
Keerati Suibkitwanchai, Adam M. Sykulski, Guillermo Perez Algorta +2
Accelerometry data has been widely used to measure activity and the circadian rhythm of individuals across the health sciences, in particular with people with advanced dementia. Mo…
Estimating the travel time and the most likely path from Lagrangian drifters
Michael O'Malley, Adam M. Sykulski, Romuald Laso-Jadart +1
We provide a novel methodology for computing the most likely path taken by drifters between arbitrary fixed locations in the ocean. We also provide an estimate of the travel time a…
The Elliptical Ornstein-Uhlenbeck Process
Adam M. Sykulski, Sofia C. Olhede, Hanna M. Sykulska-Lawrence
We introduce the elliptical Ornstein-Uhlenbeck (OU) process, which is a generalisation of the well-known univariate OU process to bivariate time series. This process maps out ellip…
Identifying and Responding to Outlier Demand in Revenue Management
Nicola Rennie, Catherine Cleophas, Adam M. Sykulski +1
Revenue management strongly relies on accurate forecasts. Thus, when extraordinary events cause outlier demand, revenue management systems need to recognise this and adapt both for…
The Debiased Spatial Whittle Likelihood
Arthur P. Guillaumin, Adam M. Sykulski, Sofia C. Olhede +1
We provide a computationally and statistically efficient method for estimating the parameters of a stochastic covariance model observed on a regular spatial grid in any number of d…
Smoothing and Interpolating Noisy GPS Data with Smoothing Splines
Jeffrey J. Early, Adam M. Sykulski
A comprehensive methodology is provided for smoothing noisy, irregularly sampled data with non-Gaussian noise using smoothing splines. We demonstrate how the spline order and tensi…
The De-Biased Whittle Likelihood
Adam M. Sykulski, Sofia C. Olhede, Arthur P. Guillaumin +2
The Whittle likelihood is a widely used and computationally efficient pseudo-likelihood. However, it is known to produce biased parameter estimates for large classes of models. We…
Exact Simulation of Noncircular or Improper Complex-Valued Stationary Gaussian Processes using Circulant Embedding
Adam M. Sykulski, Donald B. Percival
This paper provides an algorithm for simulating improper (or noncircular) complex-valued stationary Gaussian processes. The technique utilizes recently developed methods for multiv…