A Periodic State Space Model to Monthly Long-term Temperature Data

Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/15849

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DC Field	Value	Language
dc.contributor.author	Costa, Marco	pt
dc.contributor.author	Monteiro, Magda	pt
dc.date.accessioned	2016-07-05T14:21:07Z	-
dc.date.available	2016-07-05T14:21:07Z	-
dc.date.issued	2016-06	-
dc.identifier.issn	978-84-608-8178-0	-
dc.identifier.uri	http://hdl.handle.net/10773/15849	-
dc.description.abstract	This work presents a periodic state space model to model monthly temperature data. Additionally, some issues are discussed, as the parameter estimation or the Kalman filter recursions adapted to a periodic model. This framework is applied to monthly long-term temperature time series of Lisbon.	pt
dc.language.iso	eng	pt
dc.rights	openAccess	por
dc.subject	State space model	pt
dc.subject	Kalman filter	pt
dc.subject	Periodic data	pt
dc.subject	Monthly temperature	pt
dc.title	A Periodic State Space Model to Monthly Long-term Temperature Data	pt
dc.type	conferenceObject	pt
dc.peerreviewed	yes	pt
ua.publicationstatus	published	pt
ua.event.date	30 junho, 2016	pt
ua.event.type	conference	pt
degois.publication.firstPage	63	pt
degois.publication.lastPage	66	pt
degois.publication.location	Santiago de Compostela	pt
degois.publication.title	II Encontro Galaico - Portugués de Biometría, con aplicación ás Ciencias da Saúde, á Ecoloxía e ás Ciencias do Medio Ambiente (BIOAPP2016)	pt
dc.relation.publisherversion	biometria.sgapeio.es/	pt
Appears in Collections:	ESTGA - Comunicações

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