380 citations · 442 across the 15 of their papers we have counts for
15 papers
Network Time Series Models for Multivariate Volatility Forecasting
Chiara Boetti, Matthew A. Nunes
Realized volatility has become a standard tool for measuring latent variation in financial assets, and its forecasting is crucial for a wide range of financial applications. We pro…
Multiscale Dynamic Dependence Estimation over Networks
Cristian F. Jiménez-Varón, Marina I. Knight, Matthew A. Nunes
In many settings, observed multivariate time series are often nonstationary in nature, i.e., their second order properties vary over time. An additional feature is that their cross…
Network Estimation for Stationary Time Series
Madeline A. Shelley, Chiara Boetti, Marina I. Knight +1
High-dimensional multivariate time series are common in many scientific and industrial applications, where the interest lies in identifying key dependence structure within the data…
Long memory network time series
Chiara Boetti, Matthew A. Nunes, Marina I. Knight
Many scientific areas, from computer science to the environmental sciences and finance, give rise to multivariate time series which exhibit long memory, or loosely put, a slow deca…
A Doubled Adjacency Spectral Embedding Approach to Graph Clustering
Sinyoung Park, Matthew Nunes, Sandipan Roy
Spectral clustering is a popular tool in network data analysis, with applications in a variety of scientific application areas. However, many studies have shown that classical spec…
TrendLSW: Trend and Spectral Estimation of Nonstationary Time Series in R
Euan T. McGonigle, Rebecca Killick, Matthew A. Nunes
The TrendLSW R package has been developed to provide users with a suite of wavelet-based techniques to analyse the statistical properties of nonstationary time series. The key comp…
Automatic Locally Stationary Time Series Forecasting with application to predicting U.K. Gross Value Added Time Series under sudden shocks caused by the COVID pandemic
Rebecca Killick, Marina I. Knight, Guy P. Nason +2
Accurate forecasting of the U.K. gross value added (GVA) is fundamental for measuring the growth of the U.K. economy. A common nonstationarity in GVA data, such as the ABML series,…
Sequential Estimation of Temporally Evolving Latent Space Network Models
Kathryn Turnbull, Christopher Nemeth, Matthew Nunes +1
In this article we focus on dynamic network data which describe interactions among a fixed population through time. We model this data using the latent space framework, in which th…
Modelling Time-Varying First and Second-Order Structure of Time Series via Wavelets and Differencing
Euan T. McGonigle, Rebecca Killick, Matthew A. Nunes
Most time series observed in practice exhibit time-varying trend (first-order) and autocovariance (second-order) behaviour. Differencing is a commonly-used technique to remove the…
Semi-automated simultaneous predictor selection for Regression-SARIMA models
Aaron Lowther, Paul Fearnhead, Matthew Nunes +1
Deciding which predictors to use plays an integral role in deriving statistical models in a wide range of applications. Motivated by the challenges of predicting events across a te…
Generalised Network Autoregressive Processes and the GNAR package
Marina Knight, Kathryn Leeming, Guy Nason +1
This article introduces the GNAR package, which fits, predicts, and simulates from a powerful new class of generalised network autoregressive processes. Such processes consist of a…
Interpretable brain age prediction using linear latent variable models of functional connectivity
Ricardo Pio Monti, Alex Gibberd, Sandipan Roy +6
Neuroimaging-driven prediction of brain age, defined as the predicted biological age of a subject using only brain imaging data, is an exciting avenue of research. In this work we…
Dynamic detection of anomalous regions within distributed acoustic sensing data streams using locally stationary wavelet time series
Rebecca E. Wilson, Idris A. Eckley, Matthew A. Nunes +1
Distributed acoustic sensing technology is increasingly being used to support production and well management within the oil and gas sector, for example to improve flow monitoring a…
Modelling, Detrending and Decorrelation of Network Time Series
M. I. Knight, M. A. Nunes, G. P. Nason
A network time series is a multivariate time series augmented by a graph that describes how variables (or nodes) are connected. We introduce the network autoregressive (integrated)…
A Comparative Review of Dimension Reduction Methods in Approximate Bayesian Computation
M. G. B. Blum, M. A. Nunes, D. Prangle +1
Approximate Bayesian computation (ABC) methods make use of comparisons between simulated and observed summary statistics to overcome the problem of computationally intractable like…