ts_norm_gminmax: Global Min–Max Normalization

In tspredit: Time Series Prediction with Integrated Tuning

Global Min–Max Normalization

Description

Rescale values so the global minimum maps to 0 and the global maximum maps to 1 over the training set.

Usage

ts_norm_gminmax(outliers = outliers_boxplot())

Arguments

outliers

Indicate outliers transformation class. NULL can avoid outliers removal.

Details

The same scaling is applied to inputs and inverted on predictions via inverse_transform.

Value

A ts_norm_gminmax object.

References

Ogasawara, E., Murta, L., Zimbrão, G., Mattoso, M. (2009). Neural networks cartridges for data mining on time series. Proceedings of the International Joint Conference on Neural Networks (IJCNN). doi:10.1109/IJCNN.2009.5178615

Examples

# Global min–max normalization across the full training set
# Load package and example data
library(daltoolbox)
data(tsd)

# Build 10-lag windows and preview raw scale
ts <- ts_data(tsd$y, 10)
ts_head(ts, 3)
summary(ts[,10])

# Fit global min–max and transform; inspect post-scale values
preproc <- ts_norm_gminmax()
preproc <- fit(preproc, ts)
tst <- transform(preproc, ts)
ts_head(tst, 3)
summary(tst[,10])

tspredit documentation built on Feb. 11, 2026, 9:08 a.m.