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).

Version:	1.1
Depends:	R (≥ 3.0.0)
Imports:	dtw, rlist, stats, e1071, entropy, lsa
Published:	2018-07-11
DOI:	10.32614/CRAN.package.DTWBI
Author:	Camille Dezecache, T. T. Hong Phan, Emilie Poisson-Caillault
Maintainer:	Emilie Poisson-Caillault <emilie.poisson at univ-littoral.fr>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	http://mawenzi.univ-littoral.fr/DTWBI/
NeedsCompilation:	no
Citation:	DTWBI citation info
In views:	MissingData
CRAN checks:	DTWBI results

Documentation:

Reference manual:

DTWBI.html , DTWBI.pdf

Downloads:

Package source:	DTWBI_1.1.tar.gz
Windows binaries:	r-devel: DTWBI_1.1.zip, r-release: DTWBI_1.1.zip, r-oldrel: DTWBI_1.1.zip
macOS binaries:	r-release (arm64): DTWBI_1.1.tgz, r-oldrel (arm64): DTWBI_1.1.tgz, r-release (x86_64): DTWBI_1.1.tgz, r-oldrel (x86_64): DTWBI_1.1.tgz
Old sources:	DTWBI archive

Reverse dependencies:

Reverse imports:

DTWUMI

Linking:

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