minmax function

Minmax Data Normalization

The minmax() function normalizes data of the provided time series to bring values into the range [0,1]. minmax.rev() reverses the normalization.

minmax(data, max = NULL, min = NULL, byRow = FALSE)

minmax.rev(data, max, min)

Arguments

• data: A numeric vector, a univariate time series containing the values to be normalized, or a matrix with sliding windows as returned by sw.
• max: Integer indicating the maximal value in data, or a vector with the maximal values of each row (sliding window) in data. If NULL it is automatically computed.
• min: Integer indicating the minimum value in data, or a vector with the minimum values of each row (sliding window) in data. If NULL it is automatically computed.
• byRow: If TRUE, the normalization is performed by rows (sliding windows). Default set to FALSE.

Returns

data normalized between 0 and 1. If byRow is TRUE, the function returns data normalized by rows (sliding windows). max and min are returned as attributes.

Details

Ranging is done by using:

.

Examples

data(CATS)
d <- minmax(CATS[,1])
x <- minmax.rev(d, max = attributes(d)$max, min = attributes(d)$min)
all(round(x,4)==round(CATS[,1],4))

d <- minmax(sw(CATS[,1],5), byRow = TRUE)
x <- minmax.rev(d, max = attributes(d)$max, min = attributes(d)$min)
all(round(x,4)==round(sw(CATS[,1],5),4))

References

R.J. Hyndman and G. Athanasopoulos, 2013, Forecasting: principles and practice. OTexts.

E. Ogasawara, L. C. Martinez, D. De Oliveira, G. Zimbrao, G. L. Pappa, and M. Mattoso, 2010, Adaptive Normalization: A novel data normalization approach for non-stationary time series, Proceedings of the International Joint Conference on Neural Networks.

See Also

Other normalization methods: an()

Author(s)

Rebecca Pontes Salles