warigaan: Wavelet Decomposition-Based ARIMA-GARCH-ANN Hybrid Modeling

In WaveletML: Wavelet Decomposition Based Hybrid Machine Learning Models

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

• Train_fitted: Train fitted result

• Test_predicted: Test predicted result

• Accuracy: Accuracy

References

• Paul, R. K., & Garai, S. (2021). Performance comparison of wavelets-based machine learning technique for forecasting agricultural commodity prices. Soft Computing, 25(20), 12857-12873.

• Paul, R. K., & Garai, S. (2022). Wavelets based artificial neural network technique for forecasting agricultural prices. Journal of the Indian Society for Probability and Statistics, 23(1), 47-61.

• Garai, S., Paul, R. K., Rakshit, D., Yeasin, M., Paul, A. K., Roy, H. S., Barman, S. & Manjunatha, B. (2023). An MRA Based MLR Model for Forecasting Indian Annual Rainfall Using Large Scale Climate Indices. International Journal of Environment and Climate Change, 13(5), 137-150.

Examples

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

WaveletML documentation built on April 6, 2023, 1:12 a.m.