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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling source/ R- | Ghost_0.1.0.tar.gz |
9.8 KiB |
0.1.0 |
rolling linux/jammy R-4.5 | Ghost_0.1.0.tar.gz |
108.2 KiB |
0.1.0 |
rolling linux/noble R-4.5 | Ghost_0.1.0.tar.gz |
108.1 KiB |
0.1.0 |
latest source/ R- | Ghost_0.1.0.tar.gz |
9.8 KiB |
0.1.0 |
latest linux/jammy R-4.5 | Ghost_0.1.0.tar.gz |
108.2 KiB |
0.1.0 |
latest linux/noble R-4.5 | Ghost_0.1.0.tar.gz |
108.1 KiB |
0.1.0 |
2026-04-23 source/ R- | Ghost_0.1.0.tar.gz |
9.8 KiB |
0.1.0 |
2026-04-09 windows/windows R-4.5 | Ghost_0.1.0.zip |
111.1 KiB |
0.1.0 |
2025-04-20 source/ R- | Ghost_0.1.0.tar.gz |
9.8 KiB |