ts_aug_jitter {tspredit} | R Documentation |
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(sin_data)
#convert to sliding windows
xw <- ts_data(sin_data$y, 10)
#data augmentation using flip
augment <- ts_aug_jitter()
augment <- fit(augment, xw)
xa <- transform(augment, xw)
ts_head(xa)
[Package tspredit version 1.0.777 Index]