Package: DTWBI 1.2

Emilie Poisson-Caillault

DTWBI: Imputation of Time Series Based on Dynamic Time Warping

Functions to impute large gaps within time series based on Dynamic Time Warping methods. It contains all required functions to create large missing consecutive values within time series and to fill them, according to the paper Phan et al. (2017), <doi:10.1016/j.patrec.2017.08.019>. Performance criteria are added to compare similarity between two signals (query and reference).

Authors:Camille Dezecache [aut], Thi Thu Hong Phan [aut], Emilie Poisson-Caillault [aut, cre]

DTWBI_1.2.tar.gz
DTWBI_1.2.zip(r-4.7-any)DTWBI_1.2.zip(r-4.6-any)
DTWBI_1.2.tar.gz(r-4.7-any)DTWBI_1.2.tar.gz(r-4.6-any)
DTWBI_1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
DTWBI/json (API)

# Install 'DTWBI' in R:
install.packages('DTWBI', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • dataDTWBI - Six univariate signals as example for DTWBI package

On CRAN:

Conda:

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

2.29 score 1 packages 65 scripts 626 downloads 12 exports 12 dependencies

Last updated from:2ee0766fdb. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK91
source / vignettesOK155
linux-releaseOK90
windows-develOK64
windows-releaseOK73
wasm-releaseOK80

Exports:.globalfeaturescompute.fa2compute.fbcompute.fsdcompute.nmaecompute.rmsecompute.simdist_afbdtwDTWBI_univariategapCreationlocal.derivative.ddtwminCost

Dependencies:classdata.tabledtwe1071entropyjsonlitelsaproxyrlistSnowballCXMLyaml