an {TSPred}

Adaptive Normalization

Description

The an() function normalizes data of the provided time series to bring values into the range [0,1]. The function applies the method of Adaptive Normalization designed for non-stationary heteroscedastic (with non-uniform volatility) time series. an.rev() reverses the normalization.

Usage

an(data, max = NULL, min = NULL, byRow = TRUE, outlier.rm = TRUE, alpha = 1.5)

an.rev(data, max, min, an)

Arguments

data

A numeric matrix with sliding windows of time series data as returned by sw.

max

A numeric vector indicating the maximal values of each row (sliding window) in data. If NULL it is automatically computed.

min

A numeric vector indicating 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), the default.

outlier.rm

If TRUE, outlier values are removed from the data during the normalization process, the default.

alpha

The multiplier for the interquartile range used as base for outlier removal. The default is set to 1.5. The value 3.0 is also commonly used to remove only the extreme outliers.

an

The mean of each data window computed by an() and returned as attribute.

Value

data normalized between 0 and 1. max and min are returned as attributes, as well as the mean values of each row (sliding window) in data (an).

Author(s)

Rebecca Pontes Salles

References

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: minmax()

Examples

data(CATS)
swin <- sw(CATS[,1],5)
d <- an(swin, outlier.rm=FALSE)
x <- an.rev(d, max=attributes(d)$max, min=attributes(d)$min, an=attributes(d)$an)
all(round(x,4)==round(swin,4))