Description Usage Arguments Value Note
VBV moving – decompose a times series into locally estimated trend and season figures
1 | VBV.moving(n, p, q.vec, m, grundperiode, lambda1, lambda2)
|
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 |
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 |
lambda1 == lambda2 == Inf result in estimations of the original Berliner Verfahren
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