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'.
| Version: | 0.0.1 |
| Depends: | R (≥ 3.2.3) |
| Imports: | fields, MASS, stats, utils, parallel, doParallel, doSNOW, foreach |
| Suggests: | knitr, rmarkdown, keras, dummies, rlist, pROC, devtools, knitcitations, testthat, xts |
| Published: | 2017-12-04 |
| Author: | Matthew Dixon [ctb], Diego Klabjan [ctb], Lan Wei [aut, trl, cre] |
| Maintainer: | Lan Wei <lweicdsor at gmail.com> |
| License: | GPL-3 |
| URL: | https://github.com/lweicdsor/OSTSC |
| NeedsCompilation: | no |
| CRAN checks: | OSTSC results |
| Reference manual: | OSTSC.pdf |
| Vignettes: |
Over_Sampling_for_Time_Series_Classification |
| Package source: | OSTSC_0.0.1.tar.gz |
| Windows binaries: | r-devel: OSTSC_0.0.1.zip, r-release: OSTSC_0.0.1.zip, r-oldrel: OSTSC_0.0.1.zip |
| macOS binaries: | r-release: OSTSC_0.0.1.tgz, r-oldrel: OSTSC_0.0.1.tgz |
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