{"data":{"id":"10.6084/m9.figshare.1481262","type":"dois","attributes":{"doi":"10.6084/m9.figshare.1481262","prefix":"10.6084","suffix":"m9.figshare.1481262","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Aston, John A. D.","nameType":"Personal","givenName":"John A. D.","familyName":"Aston","affiliation":[],"nameIdentifiers":[]},{"name":"Nam, Christopher F. H.","nameType":"Personal","givenName":"Christopher F. H.","familyName":"Nam","affiliation":[],"nameIdentifiers":[]},{"name":"Eckley, Idris A.","nameType":"Personal","givenName":"Idris A.","familyName":"Eckley","affiliation":[],"nameIdentifiers":[]},{"name":"Killick, Rebecca","nameType":"Personal","givenName":"Rebecca","familyName":"Killick","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"The Uncertainty of Storm Season Changes: Quantifying the Uncertainty of Autocovariance Changepoints"}],"publisher":"Taylor \u0026 Francis","container":{},"publicationYear":2015,"subjects":[{"subject":"Inorganic Chemistry"},{"subject":"FOS: Chemical sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Chemical sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Cancer"},{"subject":"Mathematics"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Mathematics","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Biological Sciences"},{"subject":"Earth and Environmental Sciences"},{"subject":"Neuroscience"},{"subject":"Medicine"},{"subject":"Engineering"}],"contributors":[],"dates":[{"date":"2015-07-13","dateType":"Created"},{"date":"2015-07-13","dateType":"Updated"},{"date":"2015","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1080/00401706.2014.902776","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["297506 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"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 article 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 (LSW) framework, deriving a joint density for scale processes in the raw wavelet periodogram. By embedding this density within a hidden Markov model (HMM) 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 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