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    Rights statement: This is the peer reviewed version of the following article:Tunnicliffe Wilson, G., Haywood, J., & Petherick, L. (2021). Modeling cycles and interdependence in irregularly sampled geophysical time series. Environmetrics, e2708. https://doi.org/10.1002/env.2708 which has been published in final form at https://onlinelibrary.wiley.com/doi/10.1002/env.2708 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

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    Embargo ends: 10/11/22

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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Modeling cycles and interdependence in irregularly sampled geophysical time series

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Modeling cycles and interdependence in irregularly sampled geophysical time series. / Tunnicliffe Wilson, G.; Haywood, J.; Petherick, L.

In: Environmetrics, Vol. 33, No. 2, e2708, 31.03.2022.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Tunnicliffe Wilson, G. ; Haywood, J. ; Petherick, L. / Modeling cycles and interdependence in irregularly sampled geophysical time series. In: Environmetrics. 2022 ; Vol. 33, No. 2.

Bibtex

@article{64f4214a6a0f47e494ecabad27c1c5cd,
title = "Modeling cycles and interdependence in irregularly sampled geophysical time series",
abstract = "We show how an autoregressive Gaussian process model incorporating a time scale coefficient can be used to represent irregularly sampled geophysical time series. Selection of this coefficient, together with the order of autoregression, provides flexibility of the model appropriate to the structure of the data. This leads to a valuable improvement in the identification of the periodicities within and dependence between such series, which arise frequently and are often acquired at some cost in time and effort. We carefully explain the modeling procedure and demonstrate its efficacy for identifying periodic behavior in the context of an application to dust flux measurements from lake sediments in a region of subtropical eastern Australia. The model is further applied to the measurements of atmospheric carbon dioxide concentrations and temperature obtained from Antarctic ice cores. The model identifies periods in the glacial-interglacial cycles of these series that are associated with astronomical forcing, determines that they are causally related, and, by application to current measurements, confirms the prediction of climate warming. {\textcopyright} 2021 John Wiley & Sons Ltd.",
keywords = "causality, continuous time autoregressive model, irregular sampling, multivariate time series, resolving cycles, spectrum estimation",
author = "{Tunnicliffe Wilson}, G. and J. Haywood and L. Petherick",
note = "This is the peer reviewed version of the following article:Tunnicliffe Wilson, G., Haywood, J., & Petherick, L. (2021). Modeling cycles and interdependence in irregularly sampled geophysical time series. Environmetrics, e2708. https://doi.org/10.1002/env.2708 which has been published in final form at https://onlinelibrary.wiley.com/doi/10.1002/env.2708 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving. ",
year = "2022",
month = mar,
day = "31",
doi = "10.1002/env.2708",
language = "English",
volume = "33",
journal = "Environmetrics",
issn = "1180-4009",
publisher = "John Wiley and Sons Ltd",
number = "2",

}

RIS

TY - JOUR

T1 - Modeling cycles and interdependence in irregularly sampled geophysical time series

AU - Tunnicliffe Wilson, G.

AU - Haywood, J.

AU - Petherick, L.

N1 - This is the peer reviewed version of the following article:Tunnicliffe Wilson, G., Haywood, J., & Petherick, L. (2021). Modeling cycles and interdependence in irregularly sampled geophysical time series. Environmetrics, e2708. https://doi.org/10.1002/env.2708 which has been published in final form at https://onlinelibrary.wiley.com/doi/10.1002/env.2708 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

PY - 2022/3/31

Y1 - 2022/3/31

N2 - We show how an autoregressive Gaussian process model incorporating a time scale coefficient can be used to represent irregularly sampled geophysical time series. Selection of this coefficient, together with the order of autoregression, provides flexibility of the model appropriate to the structure of the data. This leads to a valuable improvement in the identification of the periodicities within and dependence between such series, which arise frequently and are often acquired at some cost in time and effort. We carefully explain the modeling procedure and demonstrate its efficacy for identifying periodic behavior in the context of an application to dust flux measurements from lake sediments in a region of subtropical eastern Australia. The model is further applied to the measurements of atmospheric carbon dioxide concentrations and temperature obtained from Antarctic ice cores. The model identifies periods in the glacial-interglacial cycles of these series that are associated with astronomical forcing, determines that they are causally related, and, by application to current measurements, confirms the prediction of climate warming. © 2021 John Wiley & Sons Ltd.

AB - We show how an autoregressive Gaussian process model incorporating a time scale coefficient can be used to represent irregularly sampled geophysical time series. Selection of this coefficient, together with the order of autoregression, provides flexibility of the model appropriate to the structure of the data. This leads to a valuable improvement in the identification of the periodicities within and dependence between such series, which arise frequently and are often acquired at some cost in time and effort. We carefully explain the modeling procedure and demonstrate its efficacy for identifying periodic behavior in the context of an application to dust flux measurements from lake sediments in a region of subtropical eastern Australia. The model is further applied to the measurements of atmospheric carbon dioxide concentrations and temperature obtained from Antarctic ice cores. The model identifies periods in the glacial-interglacial cycles of these series that are associated with astronomical forcing, determines that they are causally related, and, by application to current measurements, confirms the prediction of climate warming. © 2021 John Wiley & Sons Ltd.

KW - causality

KW - continuous time autoregressive model

KW - irregular sampling

KW - multivariate time series

KW - resolving cycles

KW - spectrum estimation

U2 - 10.1002/env.2708

DO - 10.1002/env.2708

M3 - Journal article

VL - 33

JO - Environmetrics

JF - Environmetrics

SN - 1180-4009

IS - 2

M1 - e2708

ER -