Re-Scale Vectors and Time-Series Features.
normaliseR
Re-Scale Vectors and Time-Series Features
Installation
You can install the development version of normaliseR
from GitHub using the following:
devtools::install_github("hendersontrent/normaliseR")
General purpose
normaliseR
is a software package for R for rescaling numerical vectors or feature_calculations
objects produced by the theft
R package for computing time-series features.
Putting calculated feature vectors on an equal scale is crucial for any statistical or machine learning model as variables with high variance can adversely impact the model’s capacity to fit the data appropriately, learn appropriate weight values, or minimise a loss function. normaliseR
includes function normalise
(or normalize
) to rescale either a whole feature_calculations
object, or a single vector of values. The following normalisation methods are currently offered:
- z-score—
"zScore"
- Sigmoid—
"Sigmoid"
- Outlier-robust Sigmoid (credit to Ben Fulcher for creating the original MATLAB version) –
"RobustSigmoid"
- Min-max—
"MinMax"
- Maximum absolute—
"MaxAbs"