Re-Scale Vectors and Time-Series Features
You can install the development version of normaliseR from GitHub
using the following:
devtools::install_github("hendersontrent/normaliseR")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"