Oversampling of imbalanced univariate time series classification data using integrated ESPO and ADASYN methods. 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. EPSO estimates the covariance structure of the minority-class samples and applies a spectral filer to reduce noise. ADASYN is a nearest neighbor interpolation approach which is subsequently applied to the EPSO samples. This code is ported from a matlab implementation by Cao et al.  and adapted for use with Recurrent Neural Networks implemented in 'TensorFlow'.
|Author||Matthew Dixon [ctb, cre], Diego Klabjan [ctb], Lan Wei [aut, trl]|
|Maintainer||Matthew Dixon <[email protected]>|
|Package repository||View on GitHub|
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