VBV.estimation: VBV estimation - estimate trend and seasonal components...

In vbv: Time series decomposition with VBV (Verallgemeinertes Berliner Verfahren)

Description

VBV estimation – estimate trend and seasonal components statically

Usage

VBV.estimation(t.vec, y.vec, p, q.vec, grundperiode, lambda1, lambda2)

Arguments

t.vec	vector of points in time as integers
y.vec	vector of data
p	maximum exponent in polynomial for trend
q.vec	vector containing frequencies to use for seasonal component, given as integers, i.e. c(1, 3, 5) for 1/2*pi, 3/2*pi, 5/2*pi (times length of base period)
grundperiode	base period in number of observations, i.e. 12 for monthly data with yearly oscillations
lambda1	penalty weight for smoothness of trend
lambda2	penalty weight for smoothness of seasonal component

Value

list with the following components:

trendschaetzer	vector of estimated trend of length length(y.vec)
saisonschaetzer	vector of estimated season of length length(y.vec)

Note

lambda1 == lambda2 == Inf result in estimations of the original Berliner Verfahren

vbv documentation built on May 2, 2019, 5:25 p.m.