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 minorityclass 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. [2011] and adapted for use with Recurrent Neural Networks implemented in 'TensorFlow'.
Package details 


Author  Matthew Dixon [ctb, cre], Diego Klabjan [ctb], Lan Wei [aut, trl] 
Maintainer  Matthew Dixon <mfrdixon@gmail.com> 
License  GPL3 
Version  0.0.1 
URL  https://github.com/lweicdsor/OSTSC 
Package repository  View on GitHub 
Installation 
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