ts_norm_swminmax: Sliding-Window Min–Max Normalization

In tspredit: Time Series Prediction with Integrated Tuning

Sliding-Window Min–Max Normalization

Description

Create an object for normalizing each window by its own min and max, preserving local contrast while standardizing scales.

Usage

ts_norm_swminmax(outliers = outliers_boxplot())

Arguments

outliers

Indicate outliers transformation class. NULL can avoid outliers removal.

Value

A ts_norm_swminmax 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

# Per-window min–max normalization for sliding windows
# 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 per-window min–max and transform; inspect post-scale values
preproc <- ts_norm_swminmax()
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.