The uncertainty of storm season changes : quantifying the uncertainty of autocovariance changepoints

Nam, Christopher and Aston, John and Eckley, Idris and Killick, Rebecca (2015) The uncertainty of storm season changes : quantifying the uncertainty of autocovariance changepoints. Technometrics, 57 (2). pp. 194-206. ISSN 0040-1706

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In oceanography, there is interest in determining storm season changes for logistical reasons such as equipment maintenance scheduling. In particular, there is interest in capturing the uncertainty associated with these changes in terms of the number and location of them. Such changes are associated with autocovariance changes. This paper proposes a framework to quantify the uncertainty of autocovariance changepoints in time series motivated by this oceanographic application. More specifically, the framework considers time series under the Locally Stationary Wavelet framework, deriving a joint density for scale processes in the raw wavelet periodogram. By embedding this density within a Hidden Markov Model framework, we consider changepoint characteristics under this multiscale setting. Such a methodology allows us to model changepoints and their uncertainty for a wide range of models, including piecewise second-order stationary processes, for example piecewise Moving Average processes.

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Journal Article
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This is an Accepted Manuscript of an article published by Taylor & Francis in Technometrics on 27/03/2014, available online:
Uncontrolled Keywords:
?? changepointshidden markov models locally stationary wavelet processes oceanography sequential monte carlomodelling and simulationapplied mathematicsstatistics and probability ??
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05 Mar 2014 09:43
Last Modified:
18 Dec 2023 01:21