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Analysis of Non-Stationary Modulated Time Series with Applications to Oceanographic Surface Flow Measurements

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

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Analysis of Non-Stationary Modulated Time Series with Applications to Oceanographic Surface Flow Measurements. / Guillaumin, Arthur P.; Sykulski, Adam M.; Olhede, Sofia C. et al.
In: Journal of Time Series Analysis, Vol. 38, No. 5, 09.2017, p. 668-710.

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

Harvard

Guillaumin, AP, Sykulski, AM, Olhede, SC, Early, JJ & Lilly, JM 2017, 'Analysis of Non-Stationary Modulated Time Series with Applications to Oceanographic Surface Flow Measurements', Journal of Time Series Analysis, vol. 38, no. 5, pp. 668-710. https://doi.org/10.1111/jtsa.12244

APA

Guillaumin, A. P., Sykulski, A. M., Olhede, S. C., Early, J. J., & Lilly, J. M. (2017). Analysis of Non-Stationary Modulated Time Series with Applications to Oceanographic Surface Flow Measurements. Journal of Time Series Analysis, 38(5), 668-710. https://doi.org/10.1111/jtsa.12244

Vancouver

Guillaumin AP, Sykulski AM, Olhede SC, Early JJ, Lilly JM. Analysis of Non-Stationary Modulated Time Series with Applications to Oceanographic Surface Flow Measurements. Journal of Time Series Analysis. 2017 Sept;38(5):668-710. Epub 2017 Jul 26. doi: 10.1111/jtsa.12244

Author

Guillaumin, Arthur P. ; Sykulski, Adam M. ; Olhede, Sofia C. et al. / Analysis of Non-Stationary Modulated Time Series with Applications to Oceanographic Surface Flow Measurements. In: Journal of Time Series Analysis. 2017 ; Vol. 38, No. 5. pp. 668-710.

Bibtex

@article{c005e70a0a9245da86b3252055765378,
title = "Analysis of Non-Stationary Modulated Time Series with Applications to Oceanographic Surface Flow Measurements",
abstract = "We propose a new class of univariate non-stationary time series models, using the framework of modulated time series, which is appropriate for the analysis of rapidly evolving time series as well as time series observations with missing data. We extend our techniques to a class of bivariate time series that are isotropic. Exact inference is often not computationally viable for time series analysis, and so we propose an estimation method based on the Whittle likelihood, a commonly adopted pseudo-likelihood. Our inference procedure is shown to be consistent under standard assumptions, as well as having considerably lower computational cost than exact likelihood in general. We show the utility of this framework for the analysis of drifting instruments, an analysis that is key to characterizing global ocean circulation and therefore also for decadal to century-scale climate understanding.",
keywords = "Missing data, Modulation, Non-stationary, Periodogram, Surface drifters, Whittle likelihood",
author = "Guillaumin, {Arthur P.} and Sykulski, {Adam M.} and Olhede, {Sofia C.} and Early, {Jeffrey J.} and Lilly, {Jonathan M.}",
year = "2017",
month = sep,
doi = "10.1111/jtsa.12244",
language = "English",
volume = "38",
pages = "668--710",
journal = "Journal of Time Series Analysis",
issn = "0143-9782",
publisher = "Wiley-Blackwell",
number = "5",

}

RIS

TY - JOUR

T1 - Analysis of Non-Stationary Modulated Time Series with Applications to Oceanographic Surface Flow Measurements

AU - Guillaumin, Arthur P.

AU - Sykulski, Adam M.

AU - Olhede, Sofia C.

AU - Early, Jeffrey J.

AU - Lilly, Jonathan M.

PY - 2017/9

Y1 - 2017/9

N2 - We propose a new class of univariate non-stationary time series models, using the framework of modulated time series, which is appropriate for the analysis of rapidly evolving time series as well as time series observations with missing data. We extend our techniques to a class of bivariate time series that are isotropic. Exact inference is often not computationally viable for time series analysis, and so we propose an estimation method based on the Whittle likelihood, a commonly adopted pseudo-likelihood. Our inference procedure is shown to be consistent under standard assumptions, as well as having considerably lower computational cost than exact likelihood in general. We show the utility of this framework for the analysis of drifting instruments, an analysis that is key to characterizing global ocean circulation and therefore also for decadal to century-scale climate understanding.

AB - We propose a new class of univariate non-stationary time series models, using the framework of modulated time series, which is appropriate for the analysis of rapidly evolving time series as well as time series observations with missing data. We extend our techniques to a class of bivariate time series that are isotropic. Exact inference is often not computationally viable for time series analysis, and so we propose an estimation method based on the Whittle likelihood, a commonly adopted pseudo-likelihood. Our inference procedure is shown to be consistent under standard assumptions, as well as having considerably lower computational cost than exact likelihood in general. We show the utility of this framework for the analysis of drifting instruments, an analysis that is key to characterizing global ocean circulation and therefore also for decadal to century-scale climate understanding.

KW - Missing data

KW - Modulation

KW - Non-stationary

KW - Periodogram

KW - Surface drifters

KW - Whittle likelihood

U2 - 10.1111/jtsa.12244

DO - 10.1111/jtsa.12244

M3 - Journal article

AN - SCOPUS:85026307003

VL - 38

SP - 668

EP - 710

JO - Journal of Time Series Analysis

JF - Journal of Time Series Analysis

SN - 0143-9782

IS - 5

ER -