FSMUMI: Imputation of Time Series Based on Fuzzy Logic

Filling large gaps in low or uncorrelated multivariate time series uses a new fuzzy weighted similarity measure. It contains all required functions to create large missing consecutive values within time series and then fill these gaps, according to the paper Phan et al. (2018), <doi:10.1155/2018/9095683>. Performance indicators are also provided to compare similarity between two univariate signals (incomplete signal and imputed signal).

Version: 1.0
Depends: R (≥ 3.0.0)
Imports: FuzzyR, stats, lsa
Published: 2018-11-26
Author: Thi-Thu-Hong Phan, Andre Bigand, Emilie Poisson-Caillault
Maintainer: Thi Thu Hong Phan <ptthong at vnua.edu.vn>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://mawenzi.univ-littoral.fr/FSMUMI/
NeedsCompilation: no
Citation: FSMUMI citation info
In views: MissingData
CRAN checks: FSMUMI results


Reference manual: FSMUMI.pdf


Package source: FSMUMI_1.0.tar.gz
Windows binaries: r-prerel: FSMUMI_1.0.zip, r-release: FSMUMI_1.0.zip, r-oldrel: FSMUMI_1.0.zip
macOS binaries: r-prerel (arm64): FSMUMI_1.0.tgz, r-release (arm64): FSMUMI_1.0.tgz, r-oldrel (arm64): FSMUMI_1.0.tgz, r-prerel (x86_64): FSMUMI_1.0.tgz, r-release (x86_64): FSMUMI_1.0.tgz


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