Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Technometrics on 27/03/2014, available online: http://wwww.tandfonline.com/10.1080/00401706.2014.902776
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Available under license: CC BY: Creative Commons Attribution 4.0 International License
Other version, 240 KB, PDF document
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - The uncertainty of storm season changes
T2 - quantifying the uncertainty of autocovariance changepoints
AU - Nam, Christopher
AU - Aston, John
AU - Eckley, Idris
AU - Killick, Rebecca
N1 - This is an Accepted Manuscript of an article published by Taylor & Francis in Technometrics on 27/03/2014, available online: http://wwww.tandfonline.com/10.1080/00401706.2014.902776
PY - 2015/7/13
Y1 - 2015/7/13
N2 - 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.
AB - 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.
KW - Changepoints
KW - Hidden Markov Models
KW - Locally Stationary Wavelet processes
KW - Oceanography
KW - Sequential Monte Carlo
U2 - 10.1080/00401706.2014.902776
DO - 10.1080/00401706.2014.902776
M3 - Journal article
VL - 57
SP - 194
EP - 206
JO - Technometrics
JF - Technometrics
SN - 0040-1706
IS - 2
ER -