# Ghost v0.1.0

0

0th

Percentile

## 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>.

## Functions in Ghost

 Name Description HTCls HTCls test_ghost_csv A simple .csv file to use in the reconstruct function. write2file_revised write2file_revised constraint_check Converting the column name to the column number of the dataset. check Checking the variables and functions. ht ht is a list of Hash objects. chr chr:Number to char identical.norowname Checking the equality of two rows of the dataset. countNulls countNulls out_csv Exporting a .csv file to the special path in pc. saxTransform saxTransform exactidentical.norowname Checking the equality of two parts of the dataset. resolveNoSignal_revised Reconstructing the missing section. hasNullRow hasNullRow reconstruct reconstruct: Missing Data Segments Imputation in Multivariate Streams addtoList addtoList sax_test A simple dataset. slidewindow slidewindow appendOffset appendOffset MissCls MissCls No Results!