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Ghost (version 0.1.0)

Missing Data Segments Imputation in Multivariate Streams

Description

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) .

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Version

Install

install.packages('Ghost')

Version

0.1.0

License

GPL-3

Last Published

March 25th, 2020

Functions in Ghost (0.1.0)

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