Nothing

```
scaleandperiods <-
function(data,scale){
# Function that receives a data frame with the time series data and
# scales it in the [0,1] interval, if desired by the user.
# The function considers that the time periods of the data appear
# as row names.
#
# IN:
#
# data <- data frame with the time series information.
# scale <- Flag that indicates if the time series data should be scaled to the
# [0,1] interval with a linear transformation as proposed by
# Nieto-Barajas and Contreras-Cristan (2014). If TRUE, then the time
# series are scaled to the [0,1] interval. Its value comes directly from
# the "scale" argument of the clustering functions.
#
# OUT:
#
# periods <- array with the time periods of the data.
# mydata <- data frame with the time series data.
# cts <- variable that indicates if some time series were removed
# because they were constant in time. If no time series were
# removed, cts = 0. If there were time series removed, cts
# indicates the column of such time series.
n <- nrow(data)
m <- ncol(data)
periods <- rownames(data)
mydata <- data
colnames(mydata) <- colnames(data)
names <- colnames(mydata)
maxima <- matrix(0,m,1)
minima <- matrix(0,m,1)
for (i in 1:m){
maxima[i,1] = max(mydata[,i])
minima[i,1] = min(mydata[,i])
}
cts <- which(maxima == minima)
if(length(cts) != 0){
cat("Removing series ",names[cts]," since they are constant.","\n")
mydata <- mydata[,-cts]
n <- nrow(mydata)
m <- ncol(mydata)
maxima <- matrix(0,m,1)
minima <- matrix(0,m,1)
for (i in 1:m){
maxima[i,1] = max(mydata[,i])
minima[i,1] = min(mydata[,i])
}
}
if(scale==TRUE){
for (j in 1:m){
m1 = maxima[j,1] - minima[j,1]
for (k in 1:n){
mydata[k,j] = 1 + (1/m1)*(mydata[k,j] - maxima[j,1])
}
}
}
return(list(periods = periods, mydata = mydata, cts = cts))
}
```

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