DTWUMI_imputation: Large gaps imputation based on DTW for multivariate signals

In DTWUMI: Imputation of Multivariate Time Series Based on Dynamic Time Warping

Large gaps imputation based on DTW for multivariate signals

Description

Fills all gaps within a multivariate signal. Gaps of size 1 are filled using the average values of nearest neighbours. Gaps of size >1 and <gap_size_threshold are filled using weighted moving average. Larger gaps are filled using DTW.

Usage

DTWUMI_imputation(
  data,
  gap_size_threshold,
  DTW_method = "DTW",
  threshold_cos = 0.995,
  thresh_cos_stop = 0.8,
  step_threshold = 2,
  ...
)

Arguments

data	a multivariate signals containing gaps
gap_size_threshold	threshold above which dtw based imputation is computed. Below this threshold, a weighted moving average is calculated
DTW_method	DTW method used for imputation ("DTW", "DDTW", "AFBDTW"). By default "DTW"
threshold_cos	threshold used to define similar sequences to the query
thresh_cos_stop	Define the lowest cosine threshold acceptable to find a similar window to the query
step_threshold	step used within the loops determining the threshold and the most similar sequence to the query
...	additional arguments from dtw() function

Value

A list containing a dataframe of completed signals.

Examples

data(dataDTWUMI)
dataDTWUMI_gap <- dataDTWUMI[["incomplete_signal"]]
imputation <- DTWUMI_imputation(dataDTWUMI_gap, gap_size_threshold = 10)
plot(dataDTWUMI_gap[, 1], type = "l", lwd = 2)
lines(imputation$output[, 1], col = "red")
plot(dataDTWUMI_gap[, 2], type = "l", lwd = 2)
lines(imputation$output[, 2], col = "red")
plot(dataDTWUMI_gap[, 3], type = "l", lwd = 2)
lines(imputation$output[, 3], col = "red")

DTWUMI documentation built on Sept. 9, 2026, 9:08 a.m.