Oversampling of imbalanced univariate time series classification data using integrated ESPO and ADASYN methods. Enhanced Structure Preserving Oversampling (ESPO) is used to generate a large percentage of the synthetic minority samples from univariate labeled time series under the modeling assumption that the predictors are Gaussian. ESPO estimates the covariance structure of the minority-class samples and applies a spectral filer to reduce noise. Adaptive Synthetic (ADASYN) sampling approach is a nearest neighbor interpolation approach which is subsequently applied to the ESPO samples. This code is ported from a 'MATLAB' implementation by Cao et al. <doi:10.1109/TKDE.2013.37> and adapted for use with Recurrent Neural Networks implemented in 'TensorFlow'.
|Author||Matthew Dixon [ctb], Diego Klabjan [ctb], Lan Wei [aut, trl, cre]|
|Maintainer||Lan Wei <[email protected]>|
|Package repository||View on CRAN|
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