| warigas {WaveletML} | R Documentation |
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
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 <- warigas(Y, ratio = 0.8, n_lag = 4)