Package: DTWUMI 1.0

POISSON-CAILLAULT Emilie

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

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.

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

DTWUMI_1.0.tar.gz
DTWUMI_1.0.tar.gz(r-4.5-noble)DTWUMI_1.0.tar.gz(r-4.4-noble)
DTWUMI_1.0.tgz(r-4.4-emscripten)DTWUMI_1.0.tgz(r-4.3-emscripten)
DTWUMI.pdf |DTWUMI.html
DTWUMI/json (API)

# Install 'DTWUMI' in R:
install.packages('DTWUMI', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • dataDTWUMI - A multivariate times series consisting of three signals as example for DTWUMI package

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 4 scripts 127 downloads 4 exports 14 dependencies

Last updated 6 years agofrom:7dfab31739. Checks:OK: 1 NOTE: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 01 2024
R-4.5-linuxNOTENov 01 2024

Exports:DTWUMI_1gap_imputationDTWUMI_imputationimp_1NAIndexes_size_missing_multi

Dependencies:classdata.tabledtwDTWBIe1071entropyjsonlitelsaMASSproxyrlistSnowballCXMLyaml