# DTWBI_univariate: DTWBI algorithm for univariate signals In DTWBI: Imputation of Time Series Based on Dynamic Time Warping

## 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

 ```1 2``` ```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 begining 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

DTWBI_univariate returns a list containing the following elements:

• output_vector: output vector containing complete data including the imputation proposal

• input_vector: original vector used as input

• query: the query i.e. the adjacent sequence to the gap

• pos_query: index of the begining and end of the query

• sim_window: vector containing the values of the most similar sequence to the query

• pos_sim_window: index of the begining and end of the similar window

• imputation_window: vector containing imputed values

• pos_imp_window: index of the begining and end of the imputation window

## Author(s)

Camille Dezecache, Hong T. T. Phan, Emilie Poisson-Caillault

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20``` ```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:imputed_data\$pos_query, col = "green", lwd = 4) # Query lines(y = imputed_data\$sim_window, x = imputed_data\$pos_sim_window:imputed_data\$pos_sim_window, col = "orange", lwd = 4) # Similar sequence to the query lines(y = imputed_data\$imputation_window, x = imputed_data\$pos_imp_window:imputed_data\$pos_imp_window, col = "blue", lwd = 4) # Imputing proposal ```

### Example output ``` "DTW"
```

DTWBI documentation built on May 2, 2019, 1:59 a.m.