OSTSC: Over Sampling for Time Series Classification
Version 0.0.1

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. and adapted for use with Recurrent Neural Networks implemented in 'TensorFlow'.

Package details

AuthorMatthew Dixon [ctb], Diego Klabjan [ctb], Lan Wei [aut, trl, cre]
Date of publication2017-12-04 15:20:31 UTC
MaintainerLan Wei <[email protected]>
URL https://github.com/lweicdsor/OSTSC
Package repositoryView on CRAN
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OSTSC documentation built on Dec. 4, 2017, 5:04 p.m.