| decomposition {VBV} | R Documentation |
decomposition - decompose a time series with VBV
Description
decomposition - decompose a time series with VBV
Usage
decomposition(t.vec, p, q.vec, base.period, lambda1, lambda2)
Arguments
t.vec |
vector of observation points. |
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 1/2pi, 3/2pi, 5/2*pi (times length of base period) |
base.period |
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:
trendA function which returns the appropriate weights if applied to a point in time
saisonA function which returns the appropriate weights if applied to a point in time
A, G1, G2Some matrices that allow to calclate SSE etc. Exposed only to reuse their calculation. See the referenced paper for details.
Examples
### Usage of decomposition
t <- 1:121 # equidistant time points, i.e. 5 days
p <- 2 # maximally quadratic
q <- c(1, 3, 5) # 'seasonal' components within the base period
base.period <- 24 # i.e. hourly data with daily cycles
l1 <- 1
l2 <- 10
dec <- decomposition( t, p, q, base.period, l1, l2)
### Note: decomosition is independent of data, only depends on time