carigaan {CEEMDANML} | R Documentation |
CEEMDAN Decomposition-Based ARIMA-GARCH-ANN Hybrid Modeling
Description
CEEMDAN Decomposition-Based ARIMA-GARCH-ANN Hybrid Modeling
Usage
carigaan(Y, ratio = 0.9, n_lag = 4)
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 |
Value
Train_fitted: Train fitted result
Test_predicted: Test predicted result
Accuracy: Accuracy
References
Garai, S., & Paul, R. K. (2023). Development of MCS based-ensemble models using CEEMDAN decomposition and machine intelligence. Intelligent Systems with Applications, 18, 200202
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 <- carigaan(Y, ratio = 0.8, n_lag = 4)
[Package CEEMDANML version 0.1.0 Index]