DTWUMI-package {DTWUMI}

Imputation of Multivariate Time Series Based on Dynamic Time Warping

Description

Functions to impute large gaps within multivariate time series based on Dynamic Time Warping methods. Gaps of size 1 or inferior to a defined threshold are filled using simple average and weighted moving average respectively. Larger gaps are filled using the methodology provided by Phan et al. (2017) <DOI:10.1109/MLSP.2017.8168165>: a query is built immediately before/after a gap and a moving window is used to find the most similar sequence to this query using Dynamic Time Warping. To lower the calculation time, similar sequences are pre-selected using global features. Contrary to the univariate method (package 'DTWBI'), these global features are not estimated over the sequence containing the gap(s), but a feature matrix is built to summarize general features of the whole multivariate signal. Once the most similar sequence to the query has been identified, the adjacent sequence to this window is used to fill the gap considered. This function can deal with multiple gaps over all the sequences componing the input multivariate signal. However, for better consistency, large gaps at the same location over all sequences should be avoided.

Details

Index of help topics:

DTWUMI-package          Imputation of Multivariate Time Series Based on
                        Dynamic Time Warping
DTWUMI_1gap_imputation
                        Imputation of a large gap based on DTW for
                        multivariate signals
DTWUMI_imputation       Large gaps imputation based on DTW for
                        multivariate signals
Indexes_size_missing_multi
                        Indexing gaps size
dataDTWUMI              A multivariate times series consisting of three
                        signals as example for DTWUMI package
imp_1NA                 Imputing gaps of size 1

Author(s)

DEZECACHE Camille, PHAN Thi Thu Hong, POISSON-CAILLAULT Emilie

Maintainer: POISSON-CAILLAULT Emilie <[e-mail]>

References

Thi-Thu-Hong Phan, Emilie Poisson-Caillault, Alain Lefebvre, Andre Bigand. Dynamic time warping-based imputation for univariate time series data. Pattern Recognition Letters, Elsevier, 2017, <DOI:10.1016/j.patrec.2017.08.019>. <hal-01609256>

Examples

data(dataDTWUMI)
dataDTWUMI_gap <- dataDTWUMI[["incomplete_signal"]]
imputation <- DTWUMI_imputation(dataDTWUMI_gap, gap_size_threshold = 10, DTW_method = "DTW")
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")