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-devel: FSMUMI_1.0.zip, r-release: FSMUMI_1.0.zip, r-oldrel: FSMUMI_1.0.zip |
| macOS binaries: | r-release (arm64): FSMUMI_1.0.tgz, r-oldrel (arm64): FSMUMI_1.0.tgz, r-release (x86_64): FSMUMI_1.0.tgz, r-oldrel (x86_64): FSMUMI_1.0.tgz |
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