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

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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A nonparametric approach to detecting changes in variance in locally stationary time series. / Chapman, Jamie-Leigh; Eckley, Idris; Killick, Rebecca.
In: Environmetrics, Vol. 31, No. 1, e2576, 01.02.2020.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Chapman, J-L, Eckley, I & Killick, R 2020, 'A nonparametric approach to detecting changes in variance in locally stationary time series', Environmetrics, vol. 31, no. 1, e2576. https://doi.org/10.1002/env.2576

APA

Chapman, J-L., Eckley, I., & Killick, R. (2020). A nonparametric approach to detecting changes in variance in locally stationary time series. Environmetrics, 31(1), Article e2576. https://doi.org/10.1002/env.2576

Vancouver

Chapman J-L, Eckley I, Killick R. A nonparametric approach to detecting changes in variance in locally stationary time series. Environmetrics. 2020 Feb 1;31(1):e2576. Epub 2019 Jun 9. doi: 10.1002/env.2576

Author

Chapman, Jamie-Leigh ; Eckley, Idris ; Killick, Rebecca. / A nonparametric approach to detecting changes in variance in locally stationary time series. In: Environmetrics. 2020 ; Vol. 31, No. 1.

Bibtex

@article{5451c2c562534091a2b547bece025682,
title = "A nonparametric approach to detecting changes in variance in locally stationary time series",
abstract = "This paper proposes a nonparametric approach to detecting changes in variance within a time series that 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 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 nonparametric setting. In addition, using a simulation study, we explore the robustness of our approach against the typical assumption of normality and presence of outliers. We illustrate the application of the approach to changes in variability of wind speeds at a location in the United Kingdom.",
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 = "2020",
month = feb,
day = "1",
doi = "10.1002/env.2576",
language = "English",
volume = "31",
journal = "Environmetrics",
issn = "1099-095X",
publisher = "John Wiley and Sons Ltd",
number = "1",

}

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 - 2020/2/1

Y1 - 2020/2/1

N2 - This paper proposes a nonparametric approach to detecting changes in variance within a time series that 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 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 nonparametric setting. In addition, using a simulation study, we explore the robustness of our approach against the typical assumption of normality and presence of outliers. We illustrate the application of the approach to changes in variability of wind speeds at a location in the United Kingdom.

AB - This paper proposes a nonparametric approach to detecting changes in variance within a time series that 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 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 nonparametric setting. In addition, using a simulation study, we explore the robustness of our approach against the typical assumption of normality and presence of outliers. We illustrate the application of the approach to changes in variability of wind speeds at a location in the United Kingdom.

KW - changepoints

KW - local stationarity

KW - wavelets

KW - wind speed

U2 - 10.1002/env.2576

DO - 10.1002/env.2576

M3 - Journal article

VL - 31

JO - Environmetrics

JF - Environmetrics

SN - 1099-095X

IS - 1

M1 - e2576

ER -