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.