Decomposition Based Machine Learning Model
CEEMDAN Based Auto Regressive Integrated Moving Average Model
Complementary Ensemble Empirical Mode Decomposition with Adaptive Nois...
CEEMDAN Based Time Delay Neural Network Model
Ensemble Empirical Mode Decomposition Based Auto Regressive Integrated...
Ensemble Empirical Mode Decomposition Based ELM Model
Ensemble Empirical Mode Decomposition Based Time Delay Neural Network ...
Empirical Mode Decomposition Based Auto Regressive Integrated Moving A...
Empirical Mode Decomposition Based ELM Model
Empirical Mode Decomposition Based Time Delay Neural Network Model
Variational Mode Decomposition Based Autoregressive Integrated Moving ...
Variational Mode Decomposition Based Extreme Learning Machine Model
Variational Mode Decomposition Based Time Delay Neural Network Model
The hybrid model is a highly effective forecasting approach that integrates decomposition techniques with machine learning to enhance time series prediction accuracy. Each decomposition technique breaks down a time series into multiple intrinsic mode functions (IMFs), which are then individually modeled and forecasted using machine learning algorithms. The final forecast is obtained by aggregating the predictions of all IMFs, producing an ensemble output for the time series. The performance of the developed models is evaluated using international monthly maize price data, assessed through metrics such as root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE). For method details see Choudhary, K. et al. (2023). <https://ssca.org.in/media/14_SA44052022_R3_SA_21032023_Girish_Jha_FINAL_Finally.pdf>.