CRAN/E | CEEMDANML

CEEMDANML

CEEMDAN Decomposition Based Hybrid Machine Learning Models

Installation

About

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.

Key Metrics

Version 0.1.0
Published 2023-04-07 372 days ago
Needs compilation? no
License GPL-3
CRAN checks CEEMDANML results

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Maintainer

Maintainer

Mr. Sandip Garai

sandipnicksandy@gmail.com

Authors

Mr. Sandip Garai

aut / cre

Dr. Ranjit Kumar Paul

aut

Dr. Md Yeasin

aut

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-develnot available

x86_64

r-releasenot available

x86_64

r-oldrelnot available

x86_64

Imports

stats
Rlibeemd
tseries
forecast
fGarch
aTSA
FinTS
LSTS
earth
caret
neuralnet
e1071
pso