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

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 , 38 (5) pp. 668-710. 10.1111/jtsa.12244. Green open access

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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.

Type: Article
Title: Analysis of Non-Stationary Modulated Time Series with Applications to Oceanographic Surface Flow Measurements
Open access status: An open access version is available from UCL Discovery
DOI: 10.1111/jtsa.12244
Publisher version: http://doi.org/10.1111/jtsa.12244
Language: English
Additional information: © 2017 Wiley Publishing Ltd. This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Modulation; non-stationary; periodogram; Whittle likelihood; missing data; surface drifters
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: http://discovery.ucl.ac.uk/id/eprint/1496574
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