14 citations · 23 across the 7 of their papers we have counts for
7 papers
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,…
The Local Partial Autocorrelation Function and Some Applications
Rebecca Killick, Marina I. Knight, Guy P. Nason +1
The classical regular and partial autocorrelation functions are powerful tools for stationary time series modelling and analysis. However, it is increasingly recognized that many t…
Rapidly evaluating lockdown strategies using spectral analysis: the cycles behind new daily COVID-19 cases and what happens after lockdown
Guy P. Nason
Spectral analysis characterises oscillatory time series behaviours such as cycles, but accurate estimation requires reasonable numbers of observations. Current COVID-19 time series…
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…
Simulation Study Comparing Two Tests of Second-order Stationarity and Confidence Intervals for Localized Autocovariance
Guy Nason
This report compares two tests of second-order stationarity through simulation. It also provides several examples of localised autocovariances and their approximate confidence inte…
Bayesian Wavelet Shrinkage of the Haar-Fisz Transformed Wavelet Periodogram
Guy P. Nason, Kara N. Stevens
It is increasingly being realised that many real world time series are not stationary and exhibit evolving second-order autocovariance or spectral structure. This article introduce…
A note on state space representations of locally stationary wavelet time series
K. Triantafyllopoulos, G. P. Nason
In this note we show that the locally stationary wavelet process can be decomposed into a sum of signals, each of which following a moving average process with time-varying paramet…