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Identifying the Best Performing Time Series Analytics for Sea Level Research

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Time Series Analysis and Forecasting

Part of the book series: Contributions to Statistics ((CONTRIB.STAT.))

Abstract

One of the most critical environmental issues confronting mankind remains the ominous spectre of climate change, in particular, the pace at which impacts will occur and our capacity to adapt. Sea level rise is one of the key artefacts of climate change that will have profound impacts on global coastal populations. Although extensive research has been undertaken into this issue, there remains considerable scientific debate about the temporal changes in mean sea level and the climatic and physical forcings responsible for them. This research has specifically developed a complex synthetic data set to test a wide range of time series methodologies for their utility to isolate a known non-linear, non-stationary mean sea level signal. This paper provides a concise summary of the detailed analysis undertaken, identifying Singular Spectrum Analysis (SSA) and multi-resolution decomposition using short length wavelets as the most robust, consistent methods for isolating the trend signal across all length data sets tested.

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Acknowledgements

The computing resources required to facilitate this research have been considerable. It would not have been possible to undertake the testing program without the benefit of access to high performance cluster computing systems. In this regard, I am indebted to John Zaitseff and Dr Zavier Barthelemy for facilitating access to the “Leonardi” and “Manning” systems at the Faculty of Engineering, University of NSW and Water Research Laboratory, respectively.

Further, this component of the research has benefitted significantly from direct consultations with some of the world’s leading time series experts and developers of method specific analysis tools. Similarly, I would like to thank the following individuals whose contributions have helped considerably to shape the final product and have ranged from providing specific and general expert advice, to guidance and review (in alphabetical order): Daniel Bowman (Department of Geological Sciences, University of North Carolina at Chapel Hill); Dr Eugene Brevdo (Research Department, Google Inc, USA); Emeritus Professor Dudley Chelton (College of Earth, Ocean and Atmospheric Sciences, Oregon State University, USA); Associate Professor Nina Golyandina (Department of Statistical Modelling, Saint Petersburg State University, Russia); Professor Rob Hyndman (Department of Econometrics and Business Statistics, Monash University, Australia); Professor Donghoh Kim (Department of Applied Statistics, Sejong University, South Korea); Alexander Shlemov (Department of Statistical Modelling, Saint Petersburg State University, Russia); Associate Professor Anton Korobeynikov (Department of Statistical Modelling, Saint Petersburg State University, Russia); Emeritus Professor Stephen Pollock (Department of Economics, University of Leicester, UK); Dr Natalya Pya (Department of Mathematical Sciences, University of Bath, UK); Dr Andrew Robinson (Department of Mathematics and Statistics, University of Melbourne, Australia); and Professor Ashish Sharma (School of Civil and Environmental Engineering, University of New South Wales, Australia).

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Authors and Affiliations

  1. School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW, 2052, Australia

    Phil. J. Watson

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  1. Phil. J. Watson
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Correspondence to Phil. J. Watson .

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Editors and Affiliations

  1. CITIC-UGR, University of Granada, Granada, Spain

    Ignacio Rojas

  2. CITIC-UGR, University of Granada, Granada, Spain

    Héctor Pomares

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Watson, P.J. (2016). Identifying the Best Performing Time Series Analytics for Sea Level Research. In: Rojas, I., Pomares, H. (eds) Time Series Analysis and Forecasting. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-28725-6_20

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