Type: Package
Title: Wavelet Decomposition Based Hybrid Machine Learning Models
Version: 0.1.0
Author: Mr. Sandip Garai [aut, cre], Dr. Ranjit Kumar Paul [aut], Dr. Md Yeasin [aut]
Maintainer: Mr. Sandip Garai <sandipnicksandy@gmail.com>
Description: 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>.
License: GPL-3
Encoding: UTF-8
Imports: stats, utils, wavelets, tseries, forecast, fGarch, aTSA, FinTS, LSTS, earth, caret, neuralnet, e1071, pso
RoxygenNote: 7.2.1
NeedsCompilation: no
Packaged: 2023-04-05 11:08:38 UTC; user
Repository: CRAN
Date/Publication: 2023-04-05 18:33:20 UTC

Wavelet Decomposition-Based ARIMA-GARCH-ANN Hybrid Modeling

Description

Wavelet Decomposition-Based ARIMA-GARCH-ANN Hybrid Modeling

Usage

warigaan(Y, ratio = 0.9, n_lag = 4, l = 6, f = 'haar')

Arguments

Y

Univariate time series

ratio

Ratio of number of observations in training and testing sets

n_lag

Lag of the provided time series data

l

Level of decomposition

f

Filter of decomposition

Value

References

Examples

Y <- rnorm(100, 100, 10)
result <- warigaan(Y, ratio = 0.8, n_lag = 4)

Wavelet Decomposition-Based ARIMA-GARCH-SVR Hybrid Modeling

Description

Wavelet Decomposition-Based ARIMA-GARCH-SVR Hybrid Modeling

Usage

warigas(Y, ratio = 0.9, n_lag = 4, l = 6, f = 'haar')

Arguments

Y

Univariate time series

ratio

Ratio of number of observations in training and testing sets

n_lag

Lag of the provided time series data

l

Level of decomposition

f

Filter of decomposition

Value

References

Examples

Y <- rnorm(100, 100, 10)
result <- warigas(Y, ratio = 0.8, n_lag = 4)