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:
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')) |
- 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.
Last updated 6 years agofrom:7dfab31739. Checks:OK: 1 NOTE: 1. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 01 2024 |
R-4.5-linux | NOTE | Dec 01 2024 |
Exports:DTWUMI_1gap_imputationDTWUMI_imputationimp_1NAIndexes_size_missing_multi
Dependencies:classdata.tabledtwDTWBIe1071entropyjsonlitelsaMASSproxyrlistSnowballCXMLyaml
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Imputation of Multivariate Time Series Based on Dynamic Time Warping | DTWUMI-package DTWUMI |
A multivariate times series consisting of three signals as example for DTWUMI package | dataDTWUMI |
Imputation of a large gap based on DTW for multivariate signals | DTWUMI_1gap_imputation |
Large gaps imputation based on DTW for multivariate signals | DTWUMI_imputation |
Imputing gaps of size 1 | imp_1NA |
Indexing gaps size | Indexes_size_missing_multi |