carigas

CEEMDAN Decomposition-Based ARIMA-GARCH-SVR Hybrid Modeling

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>.

 Mr. Sandip Garai

CEEMDANML

CEEMDAN Decomposition Based Hybrid Machine Learning Models

Dr. Ranjit Kumar Paul

Dr. Md Yeasin

carigas function

<dl><dt>Y</dt>
<dd>Univariate time series</dd>
<dt>ratio</dt>
<dd>Ratio of number of observations in training and testing sets</dd>
<dt>n_lag</dt>
<dd>Lag of the provided time series data</dd></dl>

Arguments

CEEMDAN Decomposition-Based ARIMA-GARCH-SVR Hybrid Modeling — carigas

<dl>

<dt>Y</dt>
<dd>Univariate time series</dd>


<dt>ratio</dt>
<dd>Ratio of number of observations in training and testing sets</dd>


<dt>n_lag</dt>
<dd>Lag of the provided time series data</dd>

</dl>

carigas: CEEMDAN Decomposition-Based ARIMA-GARCH-SVR Hybrid Modeling

Description

Usage

Value

Arguments

References

Examples