| Title: | CEEMDAN Decomposition Based Hybrid Machine Learning Models |
|---|---|
| Description: | Noise in the time-series data significantly affects the accuracy of the Machine Learning (ML) models (Artificial Neural Network and Support Vector Regression are considered here). Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the time series data into sub-series and help to improve the model performance. The models can achieve higher prediction accuracy than the traditional ML models. Two models have been provided here for time series forecasting. More information may be obtained from Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202>. |
| Authors: | Mr. Sandip Garai [aut, cre], Dr. Ranjit Kumar Paul [aut], Dr. Md Yeasin [aut] |
| Maintainer: | Mr. Sandip Garai <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2024-10-29 04:02:04 UTC |
| Source: | https://github.com/cran/CEEMDANML |
CEEMDAN Decomposition-Based ARIMA-GARCH-ANN Hybrid Modeling
carigaan(Y, ratio = 0.9, n_lag = 4)carigaan(Y, ratio = 0.9, n_lag = 4)
Y |
Univariate time series |
ratio |
Ratio of number of observations in training and testing sets |
n_lag |
Lag of the provided time series data |
Train_fitted: Train fitted result
Test_predicted: Test predicted result
Accuracy: Accuracy
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.
Y <- rnorm(100, 100, 10) result <- carigaan(Y, ratio = 0.8, n_lag = 4)Y <- rnorm(100, 100, 10) result <- carigaan(Y, ratio = 0.8, n_lag = 4)
CEEMDAN Decomposition-Based ARIMA-GARCH-SVR Hybrid Modeling
carigas(Y, ratio = 0.9, n_lag = 4)carigas(Y, ratio = 0.9, n_lag = 4)
Y |
Univariate time series |
ratio |
Ratio of number of observations in training and testing sets |
n_lag |
Lag of the provided time series data |
Train_fitted: Train fitted result
Test_predicted: Test predicted result
Accuracy: Accuracy
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.
Y <- rnorm(100, 100, 10) result <- carigas(Y, ratio = 0.8, n_lag = 4)Y <- rnorm(100, 100, 10) result <- carigas(Y, ratio = 0.8, n_lag = 4)