carigaan {CEEMDANML}

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)