ts_norm_ean: Adaptive Normalization with EMA

In tspredit: Time Series Prediction with Integrated Tuning

Adaptive Normalization with EMA

Description

Normalize a time series using exponentially weighted statistics that adapt to distributional changes, optionally after outlier mitigation.

Usage

ts_norm_ean(outliers = outliers_boxplot(), nw = 0)

Arguments

outliers	Indicate outliers transformation class. NULL can avoid outliers removal.
nw	windows size

Value

A ts_norm_ean object.

References

Ogasawara, E., Martinez, L. C., De Oliveira, D., Zimbrão, G., Pappa, G. L., Mattoso, M. (2010). Adaptive Normalization: A novel data normalization approach for non-stationary time series. Proceedings of the International Joint Conference on Neural Networks (IJCNN). doi:10.1109/IJCNN.2010.5596746

Examples

# time series to normalize
library(daltoolbox)
data(tsd)

# convert to sliding windows
ts <- ts_data(tsd$y, 10)
ts_head(ts, 3)
summary(ts[,10])

# normalization
preproc <- ts_norm_ean()
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.