Package: OSTSC
Title: Over Sampling for Time Series Classification
Version: 0.0.1
Author: Matthew Dixon [ctb],
    Diego Klabjan [ctb],
    Lan Wei [aut, trl, cre]
Maintainer: Lan Wei <lweicdsor@gmail.com>
Description: 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'.
Depends: R (>= 3.2.3)
License: GPL-3
URL: https://github.com/lweicdsor/OSTSC
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1.9000
Imports: fields, MASS, stats, utils, parallel, doParallel, doSNOW,
        foreach
Suggests: knitr, rmarkdown, keras, dummies, rlist, pROC, devtools,
        knitcitations, testthat, xts
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2017-11-29 19:00:31 UTC; weilan
Repository: CRAN
Date/Publication: 2017-12-04 15:20:31 UTC
