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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
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Details

Type Package
URL https://www.researchgate.net/publication/332779980_Ghost_Imputation_Accurately_Reconstructing_Missing_Data_of_the_Off_Period
License GPL-3
Encoding UTF-8
LazyData true
NeedsCompilation no
Packaged 2020-03-23 22:25:06 UTC; lorman
RoxygenNote 7.0.1
Repository CRAN
Date/Publication 2020-03-25 16:50:05 UTC

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