VBV.decomposition: VBV.decomposition - decompose a time series with VBV

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

Description

VBV.decomposition - decompose a time series with VBV

Usage

VBV.decomposition(n, p, q.vec, grundperiode, lambda1, lambda2)

Arguments

n	number of observation points. Internally this will be transformed to seq((-(n-1)/2, (n-1)/2, 1)
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 lambda1 == lambda2 == Inf result in estimations of the original Berliner Verfahren

Value

list with the following components:

trend	A function which returns the appropriate weights if applied to a point in time
saison	A function which returns the appropriate weights if applied to a point in time
A, G1, G2	Some matrices that allow to calclate SSE etc. Exposed only reuse their calculation. See the referenced paper for details.

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