ts_aug_stretch: Augmentation by stretch

In tspredit: Time Series Prediction with Integrated Tuning

Augmentation by stretch

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. stretch does data augmentation by increasing the volatility of the time series.

Usage

ts_aug_stretch(scale_factor = 1.2)

Arguments

scale_factor

for stretch

Value

a ts_aug_stretch object.

Examples

library(daltoolbox)
data(tsd)

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

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

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