DTWBI_univariate: DTWBI algorithm for univariate signals

In DTWBI: Imputation of Time Series Based on Dynamic Time Warping

DTWBI algorithm for univariate signals

Description

Imputes values of a gap of position t_gap and size T in a univariate signal based on DTW algorithm. For more details on the method, see Phan et al. (2017) DOI: <10.1016/j.patrec.2017.08.019>. Default arguments of dtw() function are used but can be manually explicited and modified.

Usage

DTWBI_univariate(
  data,
  t_gap,
  T_gap,
  DTW_method = "DTW",
  threshold_cos = NULL,
  step_threshold = NULL,
  thresh_cos_stop = 0.8,
  ...
)

Arguments

data	input vector containing a large and continuous gap (eventually derived from local.derivative.ddtw() function)
t_gap	location of the beginning of the gap (eventually extracted from gapCreation function)
T_gap	gap size (eventually extracted from gapCreation function)
DTW_method	DTW method used for imputation ("DTW", "DDTW", "AFBDTW"). By default "DTW".
threshold_cos	threshold used to define similar sequences to the query. By default, threshold_cos=0.9995 if sequence is longer than 10'000, and threshold_cos=0.995 if shorter.
step_threshold	step used within the loop determining the threshold. By default, step_threshold=50 if sequence is longer than 10'000, step_threshold=10 if sequence length is between 1'000 and 10'000. Else, step_threshold=2.
thresh_cos_stop	Define the lowest cosine threshold acceptable to find a similar window to the query. By default, thresh_cos_stop=0.8.
...	additional arguments from the dtw() function

Value

A list containing the following elements:

output_vector	Complete data including the imputation proposal
input_vector	Original input vector
query	Sequence adjacent to the gap used as the query.
pos_query	Indices of the beginning and end of the query.
sim_window	Values of the sequence most similar to the query.
pos_sim_window	Indices of the beginning and end of the similar window.
imputation_window	Imputed values.
pos_imp_window	Indices of the beginning and end of the imputation window.

Examples

data(dataDTWBI)
X <- dataDTWBI[, 1]

rate <- 0.1
output <- gapCreation(X, rate)
data <- output$output_vector
gap_begin <- output$begin_gap
gap_size <- output$gap_size
imputed_data <- DTWBI_univariate(data, t_gap=gap_begin, T_gap=gap_size)
plot(imputed_data$input_vector, type = "l", lwd = 2) # Uncomplete signal
lines(imputed_data$output_vector, col = "red") # Imputed signal
lines(y = imputed_data$query,
      x = imputed_data$pos_query[1]:imputed_data$pos_query[2],
      col = "green", lwd = 4) # Query
lines(y = imputed_data$sim_window,
      x = imputed_data$pos_sim_window[1]:imputed_data$pos_sim_window[2],
      col = "orange", lwd = 4) # Similar sequence to the query
lines(y = imputed_data$imputation_window,
      x = imputed_data$pos_imp_window[1]:imputed_data$pos_imp_window[2],
      col = "blue", lwd = 4) # Imputing proposal

DTWBI documentation built on Sept. 9, 2026, 9:07 a.m.