DTWUMI (version 1.0)

DTWUMI_imputation: 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

returns a list containing a dataframe of completed signals

Examples

# NOT RUN {
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")
# }