VBV.moving: VBV moving - decompose a times series into locally estimated...

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

Description

VBV moving – decompose a times series into locally estimated trend and season figures

Usage

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

Arguments

n	number of observation points (odd!). 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)
m	width of moving window
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:

W1	nxn matrix of weights. Trend is estimated as W1 %*% y, if y is the data vector
W2	nxn matrix of weights. Season is estimated as W2 %*% y, if y is the data vector

Note

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

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