Ensemble Empirical Mode Decomposition and Its Variant Based ELM
Model
c(person(given = "Girish Kumar",
family = "Jha",
role = c("aut", "cre"),
email = "girish.stat@gmail.com"),
person(given = "Kapil",
family = "Choudhary",
role =  c("aut", "ctb")),
person(given = "Rajeev Ranjan",
family = "Kumar",
role =  "ctb"),
person(given = "Ronit",
family = "Jaiswal",
role = "ctb"))
Forecasting univariate time series with different decomposition based Extreme Learning Machine models. For method details see Yu L, Wang S, Lai KK (2008). <doi:10.1016/j.eneco.2008.05.003>, Parida M, Behera MK, Nayak N (2018). <doi:10.1109/ICSESP.2018.8376723>.
Girish Kumar Jha [aut, cre],
Kapil Choudhary [aut, ctb],
Rajeev Ranjan Kumar [ctb],
Ronit Jaiswal [ctb]

EEMD Based LSTM Model for Time Series Forecasting
c(person(given = "Kapil",
family = "Choudhary",
role = c("aut", "cre"),
email = "kapiliasri@gmail.com"),
person(given = "Girish Kumar",
family = "Jha",
role =  c("aut", "ths", "ctb")),
person(given = "Ronit",
family = "Jaiswal",
role = c( "ctb")),
person(given = "Rajeev Ranjan ",
family = "Kumar",
role =  c( "ctb")))
Forecasting univariate time series with ensemble empirical mode decomposition (EEMD) with long short-term memory (LSTM). For method details see Jaiswal, R. et al. (2022). <doi:10.1007/s00521-021-06621-3>.
keras, tensorflow, reticulate, tsutils, BiocGenerics, utils,
graphics, magrittr,Rlibeemd, TSdeeplearning
Kapil Choudhary [aut, cre],
Girish Kumar Jha [aut, ths, ctb],
Ronit Jaiswal [ctb],
Rajeev Ranjan Kumar [ctb]

Eigenvectors from Eigenvalues Sparse Principal Component
Analysis (EESPCA)
Contains logic for computing sparse principal components via the EESPCA method,
which is based on an approximation of the eigenvector/eigenvalue identity.
Includes logic to support execution of the TPower and rifle sparse PCA methods,
as well as logic to estimate the sparsity parameters used by EESPCA, TPower and rifle
via cross-validation to minimize the out-of-sample reconstruction error.
H. Robert Frost (2021) <arXiv:2006.01924>.