ts_aug_jitter: Augmentation by jitter

In tspredit: Time Series Prediction with Integrated Tuning

Augmentation by jitter

Description

Time series data augmentation is a technique used to increase the size and diversity of a time series dataset by creating new instances of the original data through transformations or modifications. The goal is to improve the performance of machine learning models trained on time series data by reducing overfitting and improving generalization. jitter adds random noise to each data point in the time series.

Usage

ts_aug_jitter()

Value

a ts_aug_jitter object.

Examples

library(daltoolbox)
data(tsd)

#convert to sliding windows
xw <- ts_data(tsd$y, 10)

#data augmentation using flip
augment <- ts_aug_jitter()
augment <- fit(augment, xw)
xa <- transform(augment, xw)
ts_head(xa)

tspredit documentation built on June 22, 2025, 5:07 p.m.