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KFAS: Exponential Family State Space Models in R
University of Jyväskylä, Finland.ORCID iD: 0000-0001-7130-793X
2017 (English)In: Journal of Statistical Software, ISSN 1548-7660, E-ISSN 1548-7660, Vol. 78, no 10Article in journal (Refereed) Published
Abstract [en]

State space modeling is an efficient and flexible method for statistical inference of a broad class of time series and other data. This paper describes the R package KFAS for state space modeling with the observations from an exponential family, namely Gaussian, Poisson, binomial, negative binomial and gamma distributions. After introducing the basic theory behind Gaussian and non-Gaussian state space models, an illustrative example of Poisson time series forecasting is provided. Finally, a comparison to alternative R packages suitable for non-Gaussian time series modeling is presented.

Place, publisher, year, edition, pages
Foundation for Open Access Statistic , 2017. Vol. 78, no 10
Keywords [en]
R, exponential family, state space models, time series, forecasting, dynamic linear models
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-144911DOI: 10.18637/jss.v078.i10OAI: oai:DiVA.org:liu-144911DiVA, id: diva2:1180674
Available from: 2018-02-06 Created: 2018-02-06 Last updated: 2018-02-06

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Helske, Jouni
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  • de-DE
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  • nn-NB
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