10 citations · 22 across the 6 of their papers we have counts for
6 papers
MOPED: A moving sum method for change point detection in pairwise extremal dependence
Euan T. McGonigle, Matthew Pawley, Jordan Richards +1
It is increasingly the case with modern time series that many data sets of practical interest contain abrupt changes in structure. These changes may occur in complex characteristic…
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…
Nonparametric data segmentation in multivariate time series via joint characteristic functions
Euan T. McGonigle, Haeran Cho
Modern time series data often exhibit complex dependence and structural changes which are not easily characterised by shifts in the mean or model parameters. We propose a nonparame…
Robust multiscale estimation of time-average variance for time series segmentation
Euan T. McGonigle, Haeran Cho
There exist several methods developed for the canonical change point problem of detecting multiple mean shifts, which search for changes over sections of the data at multiple scale…
Subspace Change-Point Detection via Low-Rank Matrix Factorisation
Euan Thomas McGonigle, Hankui Peng
Multivariate time series can often have a large number of dimensions, whether it is due to the vast amount of collected features or due to how the data sources are processed. Frequ…
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…