scale: Time Series Scaling

In IncDTW: Incremental Calculation of Dynamic Time Warping

Time Series Scaling

Description

scales a time series per dimension/column.

Usage

scale(x, type = c('z', '01'), threshold = 1e-5,
     xmean = NULL, xsd = NULL, xmin = NULL, xmax = NULL)

# deprecated
norm(x, type = c('z', '01'), threshold = 1e-5,
     xmean = NULL, xsd = NULL, xmin = NULL, xmax = NULL)

Arguments

x	time series as vector or matrix
type	character, describes the method how to scale (or normalize) the time series (per column if x is multivariate). The parameter type is either 'z' for z-scaling or '01' for max-min scaling.
threshold	double, defines the minimum value of the standard deviation, or difference of minimum and maximum. If this value is smaller than the threshold, then no scaling is performed, only shifting by the mean or minimum, respectively. Default value = 1e-5.
xmean	mean used for z-scaling. See details.
xsd	standard deviation used for z-scaling. See details.
xmin	minimum used for 0-1 scaling. See details.
xmax	maximum used for 0-1 scaling. See details.

Details

For a vector x the z-scaling subtracts the mean and devides by the standard deviation: of (x-mean(x))/sd(x). The min-max scaling performs (x-min(x))/(max(x)-min(x)).

The parameters xmean, xsd, xmin, can be set xmax or passed as NULL (= default value). If these values are NULL, they are calculated based on x.

Value

x	the scaled vector or matrix

Examples

# min-max scaling or z-scaling for a vector 
x <- cumsum(rnorm(100, 10, 5))
y <- scale(x, "01")
z <- scale(x, "z")
par(mfrow = c(1, 3))
plot(x, type="l")
plot(y, type="l")
plot(z, type="l")
par(mfrow = c(1, 1))


# columnwise for matrices
x <- matrix(c(1:10, sin(1:10)), ncol = 2)
y <- scale(x, "01")
z <- scale(x, "z")
par(mfrow = c(1, 3))
matplot(x, type="l")
matplot(y, type="l")
matplot(z, type="l")
par(mfrow = c(1, 1))


# IncDTW::scale() and base::scale() perform same z-scaling
x <- cumsum(rnorm(100))
xi <- IncDTW::scale(x, type = "z")
xb <- base::scale(x, TRUE, TRUE)
sum(abs(xi-xb))

IncDTW documentation built on March 18, 2022, 6:43 p.m.