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Abstract Details
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Activity Number:
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109
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Type:
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Topic Contributed
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Date/Time:
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Monday, July 30, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Business and Economic Statistics Section
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| Abstract - #304791 |
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Title:
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Change Point Detection for Marine Energy Series
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Author(s):
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Rebecca Killick*+
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Companies:
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Lancaster University
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Address:
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Mathematics & Statistics, Lancaster, _, LA1 4YF, United Kingdom
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Keywords:
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wavelets ;
autocovariance ;
non-stationary ;
wave energy converter
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Abstract:
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We consider the problem of detecting changes in general autocovariance structure within marine energy data sets. An efficient and accurate analysis of such data is of considerable interest to those working in the energy sector as understanding the changing characteristics of marine energy is central to reliable design and development of energy converters. Detecting the presence of changepoints in marine energy time-series is of particular importance, since statistical and engineering modelling of the marine environment typically assumes stationarity of the environment (in time). Drawing on recent work for detecting autocovariance changes in Killick et al. (2012) we consider changes in autocovariance within a multi-axis wave energy converter.
References
Killick, R., Eckley, I. A., and Jonathan, P. (2012). Detecting changes in second order structure within oceanographic time series. In Submission.
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