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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>.
Getting started
Browse package contents
Package details |
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| Author | Siyavash Shabani, Reza Rawassizadeh |
| Maintainer | Siyavash Shabani <s.shabani.aut@gmail.com> |
| License | GPL-3 |
| Version | 0.1.0 |
| URL | https://www.researchgate.net/publication/332779980_Ghost_Imputation_Accurately_Reconstructing_Missing_Data_of_the_Off_Period |
| Package repository | View on CRAN |
| Installation |
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