ts_aug_jitter: Augmentation by Jitter

In tspredit: Time Series Prediction with Integrated Tuning

Augmentation by Jitter

Description

Time series augmentation by adding low-amplitude random noise to each point to increase robustness and reduce overfitting.

Usage

ts_aug_jitter()

Details

Noise scale is estimated from within-window deviations.

Value

A ts_aug_jitter object.

References

• J. T. Um et al. (2017). Data augmentation of wearable sensor data for Parkinson’s disease monitoring using convolutional neural networks.

• Q. Wen et al. (2021). Time Series Data Augmentation for Deep Learning: A Survey. IJCAI Workshop on Time Series.

Examples

# Jitter augmentation with noise estimated from windows
# Load package and example dataset
library(daltoolbox)
data(tsd)

# Convert to sliding windows and preview
xw <- ts_data(tsd$y, 10)
ts_head(xw)

# Apply jitter (adds small noise; keeps target column unchanged)
augment <- ts_aug_jitter()
augment <- fit(augment, xw)
xa <- transform(augment, xw)
ts_head(xa)

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