warigas

Wavelet Decomposition-Based ARIMA-GARCH-SVR Hybrid Modeling

Wavelet decomposes a series into multiple sub series called detailed and smooth components which helps to capture volatility at multi resolution level by various models. Two hybrid Machine Learning (ML) models (Artificial Neural Network and Support Vector Regression have been used) have been developed in combination with stochastic models, feature selection, and optimization algorithms for prediction of the data. The algorithms have been developed following Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.

 Mr. Sandip Garai

WaveletML

Wavelet Decomposition Based Hybrid Machine Learning Models

Dr. Ranjit Kumar Paul

Dr. Md Yeasin

warigas 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>
<dt>l</dt>
<dd>Level of decomposition</dd>
<dt>f</dt>
<dd>Filter of decomposition</dd></dl>

Arguments

Wavelet Decomposition-Based ARIMA-GARCH-SVR Hybrid Modeling — warigas

<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>


<dt>l</dt>
<dd>Level of decomposition</dd>


<dt>f</dt>
<dd>Filter of decomposition</dd>

</dl>

warigas: Wavelet Decomposition-Based ARIMA-GARCH-SVR Hybrid Modeling

Description

Usage

Value

Arguments

References

Examples