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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 |
| Reference manual: | Ghost.pdf |
| 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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