6533b86efe1ef96bd12cac28
RESEARCH PRODUCT
KFAS : Exponential Family State Space Models in R
Jouni Helskesubject
FOS: Computer and information sciencesStatistics and ProbabilityaikasarjatGaussianNegative binomial distributionforecastingPoisson distribution01 natural sciencesStatistics - ComputationMethodology (stat.ME)010104 statistics & probability03 medical and health sciencessymbols.namesake0302 clinical medicineExponential familyexponential familyGamma distributionStatistical inferenceState spaceApplied mathematicsSannolikhetsteori och statistik030212 general & internal medicine0101 mathematicsProbability Theory and Statisticslcsh:Statisticslcsh:HA1-4737Computation (stat.CO)Statistics - MethodologyMathematicsR; exponential family; state space models; time series; forecasting; dynamic linear modelsta112state space modelsSeries (mathematics)RStatistics; Computer softwaresymbolsStatistics Probability and Uncertaintytime seriesSoftwaredynamic linear modelsdescription
State space modelling is an efficient and flexible method for statistical inference of a broad class of time series and other data. This paper describes an R package KFAS for state space modelling 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 modelling is presented.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-01-01 |