R/ts_aug_jitter.R

In tspredit: Time Series Prediction with Integrated Tuning

#'@title 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.
#'@return a `ts_aug_jitter` object.
#'@examples
#'library(daltoolbox)
#'data(tsd)
#'
#'#convert to sliding windows
#'xw <- ts_data(tsd$y, 10)
#'
#'#data augmentation using flip
#'augment <- ts_aug_jitter()
#'augment <- fit(augment, xw)
#'xa <- transform(augment, xw)
#'ts_head(xa)
#'@importFrom daltoolbox dal_transform
#'@importFrom daltoolbox fit
#'@importFrom daltoolbox transform
#'@export
ts_aug_jitter <- function() {
  obj <- dal_transform()
  obj$preserve_data <- TRUE
  class(obj) <- append("ts_aug_jitter", class(obj))
  return(obj)
}

#'@importFrom stats sd
#'@importFrom daltoolbox fit
#'@exportS3Method fit ts_aug_jitter
fit.ts_aug_jitter <- function(obj, data, ...) {
  an <- apply(data, 1, mean)
  x <- data - an
  obj$sd <- stats::sd(x)
  return(obj)
}

#'@importFrom stats rnorm
#'@importFrom daltoolbox transform
#'@exportS3Method transform ts_aug_jitter
transform.ts_aug_jitter <- function(obj, data, ...) {
  add.ts_aug_jitter <- function(obj, data) {
    x <- stats::rnorm(length(data), mean = 0, sd = obj$sd)
    x <- matrix(x, nrow=nrow(data), ncol=ncol(data))
    x[,ncol(data)] <- 0
    data <- data + x
    attr(data, "idx") <- 1:nrow(data)
    return(data)
  }
  result <- add.ts_aug_jitter(obj, data)
  if (obj$preserve_data) {
    idx <- c(1:nrow(data), attr(result, "idx"))
    result <- rbind(data, result)
    result <- adjust_ts_data(result)
    attr(result, "idx") <- idx
  }
  return(result)
}