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

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@article{5451c2c562534091a2b547bece025682,
title = "A nonparametric approach to detecting changes in variance in locally stationary time series",
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.",
keywords = "changepoints, local stationarity, wavelets, wind speed",
author = "Jamie-Leigh Chapman and Idris Eckley and Rebecca Killick",
note = "https://onlinelibrary.wiley.com/doi/full/10.1002/env.2576",
year = "2019",
month = "6",
day = "9",
doi = "10.1002/env.2576",
language = "English",
journal = "Environmetrics",
issn = "1180-4009",
publisher = "John Wiley and Sons Ltd",

}

RIS

TY - JOUR

T1 - A nonparametric approach to detecting changes in variance in locally stationary time series

AU - Chapman, Jamie-Leigh

AU - Eckley, Idris

AU - Killick, Rebecca

N1 - https://onlinelibrary.wiley.com/doi/full/10.1002/env.2576

PY - 2019/6/9

Y1 - 2019/6/9

N2 - 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.

AB - 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.

KW - changepoints

KW - local stationarity

KW - wavelets

KW - wind speed

U2 - 10.1002/env.2576

DO - 10.1002/env.2576

M3 - Journal article

JO - Environmetrics

JF - Environmetrics

SN - 1180-4009

M1 - e2576

ER -