Ghost: Missing Data Segments Imputation in Multivariate Streams

Helper functions provide an accurate imputation algorithm for reconstructing the missing segment in a multi-variate data streams. Inspired by single-shot learning, it reconstructs the missing segment by identifying the first similar segment in the stream. Nevertheless, there should be one column of data available, i.e. a constraint column. The values of columns can be characters (A, B, C, etc.). The result of the imputed dataset will be returned a .csv file. For more details see Reza Rawassizadeh (2019) <doi:10.1109/TKDE.2019.2914653>.

Version:	0.1.0
Depends:	R (≥ 2.10)
Imports:	R6
Published:	2020-03-25
DOI:	10.32614/CRAN.package.Ghost
Author:	Siyavash Shabani, Reza Rawassizadeh
Maintainer:	Siyavash Shabani <s.shabani.aut at gmail.com>
License:	GPL-3
URL:	https://www.researchgate.net/publication/332779980_Ghost_Imputation_Accurately_Reconstructing_Missing_Data_of_the_Off_Period
NeedsCompilation:	no
CRAN checks:	Ghost results

Documentation:

Reference manual:

Ghost.pdf

Downloads:

Package source:	Ghost_0.1.0.tar.gz
Windows binaries:	r-devel: Ghost_0.1.0.zip, r-release: Ghost_0.1.0.zip, r-oldrel: Ghost_0.1.0.zip
macOS binaries:	r-release (arm64): Ghost_0.1.0.tgz, r-oldrel (arm64): Ghost_0.1.0.tgz, r-release (x86_64): Ghost_0.1.0.tgz, r-oldrel (x86_64): Ghost_0.1.0.tgz

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