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Trend Locally Stationary Wavelet Processes

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

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Trend Locally Stationary Wavelet Processes. / McGonigle, Euan; Killick, Rebecca; Nunes, Matthew.
In: Journal of Time Series Analysis, Vol. 43, No. 6, 30.11.2022, p. 895-917.

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

Harvard

McGonigle, E, Killick, R & Nunes, M 2022, 'Trend Locally Stationary Wavelet Processes', Journal of Time Series Analysis, vol. 43, no. 6, pp. 895-917. https://doi.org/10.1111/jtsa.12643

APA

McGonigle, E., Killick, R., & Nunes, M. (2022). Trend Locally Stationary Wavelet Processes. Journal of Time Series Analysis, 43(6), 895-917. https://doi.org/10.1111/jtsa.12643

Vancouver

McGonigle E, Killick R, Nunes M. Trend Locally Stationary Wavelet Processes. Journal of Time Series Analysis. 2022 Nov 30;43(6):895-917. Epub 2022 Mar 2. doi: 10.1111/jtsa.12643

Author

McGonigle, Euan ; Killick, Rebecca ; Nunes, Matthew. / Trend Locally Stationary Wavelet Processes. In: Journal of Time Series Analysis. 2022 ; Vol. 43, No. 6. pp. 895-917.

Bibtex

@article{71f9f671e5224032b7becdf7bb33fc67,
title = "Trend Locally Stationary Wavelet Processes",
abstract = "Most time series observed in practice exhibit first as well as second-order nonstationarity. In this article we propose a novel framework for modelling series with simultaneous time-varying first and second-order structure, removing the restrictive zero-mean assumption of locally stationary wavelet processes and extending the applicability of the locally stationary wavelet model to include trend components. We develop an associated estimation theory for both first and second order time series quantities and show that our estimators achieve good properties in isolation of each other by making appropriate assumptions on the series trend. We demonstrate the utility of the method by analysing the global mean sea temperature time series, highlighting the impact of the changing climate.",
keywords = "Climate data, locally stationary, non-stationary time series, trend estimation, wavelet spectrum",
author = "Euan McGonigle and Rebecca Killick and Matthew Nunes",
year = "2022",
month = nov,
day = "30",
doi = "10.1111/jtsa.12643",
language = "English",
volume = "43",
pages = "895--917",
journal = "Journal of Time Series Analysis",
issn = "0143-9782",
publisher = "Wiley-Blackwell",
number = "6",

}

RIS

TY - JOUR

T1 - Trend Locally Stationary Wavelet Processes

AU - McGonigle, Euan

AU - Killick, Rebecca

AU - Nunes, Matthew

PY - 2022/11/30

Y1 - 2022/11/30

N2 - Most time series observed in practice exhibit first as well as second-order nonstationarity. In this article we propose a novel framework for modelling series with simultaneous time-varying first and second-order structure, removing the restrictive zero-mean assumption of locally stationary wavelet processes and extending the applicability of the locally stationary wavelet model to include trend components. We develop an associated estimation theory for both first and second order time series quantities and show that our estimators achieve good properties in isolation of each other by making appropriate assumptions on the series trend. We demonstrate the utility of the method by analysing the global mean sea temperature time series, highlighting the impact of the changing climate.

AB - Most time series observed in practice exhibit first as well as second-order nonstationarity. In this article we propose a novel framework for modelling series with simultaneous time-varying first and second-order structure, removing the restrictive zero-mean assumption of locally stationary wavelet processes and extending the applicability of the locally stationary wavelet model to include trend components. We develop an associated estimation theory for both first and second order time series quantities and show that our estimators achieve good properties in isolation of each other by making appropriate assumptions on the series trend. We demonstrate the utility of the method by analysing the global mean sea temperature time series, highlighting the impact of the changing climate.

KW - Climate data

KW - locally stationary

KW - non-stationary time series

KW - trend estimation

KW - wavelet spectrum

U2 - 10.1111/jtsa.12643

DO - 10.1111/jtsa.12643

M3 - Journal article

VL - 43

SP - 895

EP - 917

JO - Journal of Time Series Analysis

JF - Journal of Time Series Analysis

SN - 0143-9782

IS - 6

ER -