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A nonparametric approach to detecting changes in variance in locally stationary time series

Research output: Contribution to journalJournal article

E-pub ahead of print
Article numbere2576
<mark>Journal publication date</mark>9/06/2019
<mark>Journal</mark>Environmetrics
Number of pages12
Publication statusE-pub ahead of print
Early online date9/06/19
Original languageEnglish

Abstract

This article proposes a nonparametric approach to detecting changes in variance within a time series which we demonstrate is resilient to departures from the assumption of Normality or presence of outliers. Our method is founded on a local estimate of the variance provided by the Locally Stationary Wavelet (LSW) framework. Within this setting, the structure of this local estimate of the variance will be piecewise constant if a time series has piecewise constant variance. Consequently, changes in the variance of a time series can be detected in a non-parametric setting.
In addition, using a simulation study, we explore the robustness of our approach against the typical assumption of Normality and to the presence of outliers. We illustrate the application of the approach to changes in variability of wind speeds at a location in the UK.

Bibliographic note

https://onlinelibrary.wiley.com/doi/full/10.1002/env.2576