Package: CEEMDANML
Type: Package
Title: CEEMDAN Decomposition Based Hybrid Machine Learning Models
Version: 0.1.0
Authors@R: c(person(" Mr. Sandip", "Garai", role = c("aut","cre"), email = "sandipnicksandy@gmail.com"),
    person("Dr. Ranjit Kumar", "Paul", role = "aut", email = "ranjitstat@gmail.com"),
    person("Dr. Md", "Yeasin", role = "aut", email = "yeasin.iasri@gmail.com"))
Author: Mr. Sandip Garai [aut, cre], Dr. Ranjit Kumar Paul [aut], Dr.
        Md Yeasin [aut]
Maintainer: Mr. Sandip Garai <sandipnicksandy@gmail.com>
Description: Noise in the time-series data significantly affects the
        accuracy of the Machine Learning (ML) models (Artificial Neural
        Network and Support Vector Regression are considered here).
        Complete Ensemble Empirical Mode Decomposition with Adaptive
        Noise (CEEMDAN) decomposes the time series data into sub-series
        and help to improve the model performance. The models can
        achieve higher prediction accuracy than the traditional ML
        models. Two models have been provided here for time series
        forecasting. More information may be obtained from Garai and
        Paul (2023) <doi:10.1016/j.iswa.2023.200202>.
License: GPL-3
Encoding: UTF-8
Imports: stats, Rlibeemd, tseries, forecast, fGarch, aTSA, FinTS, LSTS,
        earth, caret, neuralnet, e1071, pso
RoxygenNote: 7.2.1
NeedsCompilation: no
Packaged: 2026-10-10 13:39:32 UTC; root
Config/pak/sysreqs: libgsl0-dev libicu-dev libssl-dev
Repository: https://sandipgarai.r-universe.dev
Date/Publication: 2023-04-07 07:30:02 UTC
RemoteUrl: https://github.com/cran/CEEMDANML
RemoteRef: HEAD
RemoteSha: 6ca7dfa85ad0e974fdb573cbd64c07f58148d49b