VMDELM: Variational Mode Decomposition Based Extreme Learning Machine...

In vmdTDNN: VMD Based Time Delay Neural Network Model

Variational Mode Decomposition Based Extreme Learning Machine Model

Description

The VMDELM function computes forecasted value with different forecasting evaluation criteria for Variational Mode Decomposition (VMD) Based Extreme learning machine (ELM).

Usage

VMDELM (data, stepahead=10, nIMF=4, alpha=2000, tau=0, D=FALSE)

Arguments

data	Input univariate time series (ts) data.
stepahead	The forecast horizon.
nIMF	The number of IMFs.
alpha	The balancing parameter.
tau	Time-step of the dual ascent.
D	a boolean.

Details

This function decomposes a nonlinear, nonstationary time series into different IMFs using VMD (Qian et al., 2019). Extreme learning machine (ELM) is used to forecast decomposed IMFs individually. Finally, the prediction results of all three components are aggregated to formulate an ensemble output for the input time series.

Value

AllIMF	List of all IMFs with residual for input series.
data_test	Testing set used to measure the out of sample performance.
AllIMF_forecast	Forecasted value of all individual IMF
FinalVMDELM_forecast	Final forecasted value of the VMD based ELM model. It is obtained by combining the forecasted value of all individual IMF.
MAE_VMDELM	Mean Absolute Error (MAE) for VMDELM model.
MAPE_VMDELM	Mean Absolute Percentage Error (MAPE) for VMDELM model.
rmse_VMDELM	Root Mean Square Error (RMSE) for VMDELM model.

References

Dragomiretskiy, K.and Zosso, D. (2014). Variational mode decomposition. IEEE transactions on signal processing, 62(3), 531–544.

Shao, Z., Chao, F., Yang, S. L., & Zhou, K. L. (2017). A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting. Renewable and Sustainable Energy Reviews, 75, 123–136.

Qian, Z., Pei, Y., Zareipour, H. andChen, N. (2019). A review and discussion of decomposition-based hybrid models for wind energy forecasting applications. Applied energy, 235, 939–953.

See Also

VMDTDNN,VMDARIMA

Examples

data("Data_Maize")
VMDELM(Data_Maize)

vmdTDNN documentation built on Aug. 8, 2022, 5:07 p.m.