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
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

Downloads:

Reference manual: Ghost.pdf
Package source: Ghost_0.1.0.tar.gz
Windows binaries: r-devel: Ghost_0.1.0.zip, r-devel-gcc8: Ghost_0.1.0.zip, r-release: Ghost_0.1.0.zip, r-oldrel: Ghost_0.1.0.zip
OS X binaries: r-release: Ghost_0.1.0.tgz, r-oldrel: not available

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