Cost functions for the Least Absolution Deviation from a given quantile
Details
this is a very niaive and slow implimentation
Methods
Get the length of time series
Method new()
Initialise the cost function
Arguments
x
numeric vector of observations
m
expected value of x
tau
the quantile
Method baseCost()
Compute the non-anomalous cost of a segment
Usage
ladCost$baseCost(a, b, pen = 0)
Arguments
a
start of period
b
end of period
pen
penalty cost
Method pointCost()
Compute the point anomaly cost of a time step
Usage
ladCost$pointCost(a, pen)
Arguments
a
time step
pen
penalty cost
Method collectiveCost()
Compute the anomalous cost of a segment
Usage
ladCost$collectiveCost(a, b, pen, len)
Arguments
a
start of period
b
end of period
pen
penalty cost
len
minimum number of observations
Compute parameters of a segment if anomalous
Arguments
a
start of period
b
end of period
Method clone()
The objects of this class are cloneable with this method.
Usage
ladCost$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.
Examples
set.seed(0)
m <- runif(100)
x <- rnorm(100,m)
p <- ladCost$new(x,m,0.5)
p$baseCost(90,95) ## cost of non-anomalous distribution for x[90:95]
#> [1] -12.48888
p$pointCost(90,0) ## point anomaly cost for x[90] with 0 penalty
#> [1] -2.772589
## collective anomaly cost for x[90:95] with penalty of 57 and at least 3 observation
p$collectiveCost(90,95,57,3)
#> [1] 43.86362