Non-Gaussian Autoregressive Processes with Tukey g-and-h Transformations

Handle URI:
http://hdl.handle.net/10754/626528
Title:
Non-Gaussian Autoregressive Processes with Tukey g-and-h Transformations
Authors:
Yan, Yuan; Genton, Marc G. ( 0000-0001-6467-2998 )
Abstract:
When performing a time series analysis of continuous data, for example from climate or environmental problems, the assumption that the process is Gaussian is often violated. Therefore, we introduce two non-Gaussian autoregressive time series models that are able to fit skewed and heavy-tailed time series data. Our two models are based on the Tukey g-and-h transformation. We discuss parameter estimation, order selection, and forecasting procedures for our models and examine their performances in a simulation study. We demonstrate the usefulness of our models by applying them to two sets of wind speed data.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Publisher:
arXiv
KAUST Grant Number:
OSR-2015-CRG4-2640
Issue Date:
20-Nov-2017
ARXIV:
arXiv:1711.07516
Type:
Preprint
Additional Links:
http://arxiv.org/abs/1711.07516v1; http://arxiv.org/pdf/1711.07516v1
Appears in Collections:
Other/General Submission; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorYan, Yuanen
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2017-12-28T07:32:15Z-
dc.date.available2017-12-28T07:32:15Z-
dc.date.issued2017-11-20en
dc.identifier.urihttp://hdl.handle.net/10754/626528-
dc.description.abstractWhen performing a time series analysis of continuous data, for example from climate or environmental problems, the assumption that the process is Gaussian is often violated. Therefore, we introduce two non-Gaussian autoregressive time series models that are able to fit skewed and heavy-tailed time series data. Our two models are based on the Tukey g-and-h transformation. We discuss parameter estimation, order selection, and forecasting procedures for our models and examine their performances in a simulation study. We demonstrate the usefulness of our models by applying them to two sets of wind speed data.en
dc.publisherarXiven
dc.relation.urlhttp://arxiv.org/abs/1711.07516v1en
dc.relation.urlhttp://arxiv.org/pdf/1711.07516v1en
dc.rightsArchived with thanks to arXiven
dc.titleNon-Gaussian Autoregressive Processes with Tukey g-and-h Transformationsen
dc.typePreprinten
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.eprint.versionPre-printen
dc.identifier.arxividarXiv:1711.07516en
kaust.authorYan, Yuanen
kaust.authorGenton, Marc G.en
kaust.grant.numberOSR-2015-CRG4-2640en
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