minmax: Minmax Data Normalization
In RebeccaSalles/TSPred: Functions for Benchmarking Time Series Prediction

Description Usage Arguments Details Value Author(s) References See Also Examples

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

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

minmax.rev(data, max, min)

`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`.

Ranging is done by using:

X' = \frac{(x - x_{min})}{(x_{max} - x_{min})}

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.

Rebecca Pontes Salles

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.

Other normalization methods: an()

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))