VMDTDNN: Variational Mode Decomposition Based Time Delay Neural...

In vmdTDNN: VMD Based Time Delay Neural Network Model

Variational Mode Decomposition Based Time Delay Neural Network Model

Description

The VMDTDNN function computes forecasted value with different forecasting evaluation criteria for Variational Mode Decomposition (VMD) Based Time Delay Neural Network Model (TDNN).

Usage

VMDTDNN (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

The Variational Mode Decomposition method is a novel adaptive, non-recursive signal decomposition technology, which was introduced by Dragomiretskiy and Zosso (2014). VMD method helps to solve current decomposition methods limitation such as lacking mathematical theory, recursive sifting process which not allows for backward error correction, hard-band limits, the requirement to predetermine filter bank boundaries, and sensitivity to noise. It decomposes a series into sets of IMFs. Time-delay neural networks are used to forecast decomposed components individually (Jha and Sinha, 2014). Finally, the prediction results of all 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
FinalVMDTDNN_forecast	Final forecasted value of the VMD based TDNN model. It is obtained by combining the forecasted value of all individual IMF.
MAE_VMDTDNN	Mean Absolute Error (MAE) for VMDTDNN model.
MAPE_VMDTDNN	Mean Absolute Percentage Error (MAPE) for VMDTDNN model.
rmse_VMDTDNN	Root Mean Square Error (RMSE) for VMDTDNN model.

References

Choudhury, K., Jha, G. K., Das, P. and Chaturvedi, K. K. (2019). Forecasting potato price using ensemble artificial neural networks. Indian Journal of Extension Education, 55(1), 73–77.

Choudhary, K., Jha, G. K., Kumar, R. R. and Mishra, D. C. (2019). Agricultural commodity price analysis using ensemble empirical mode decomposition: A case study of daily potato price series. Indian Journal of Agricultural Sciences, 89(5), 882–886.

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

Jha, G. K. and Sinha, K. (2014). Time-delay neural networks for time series prediction: An application to the monthly wholesale price of oilseeds in India. Neural Computing and Applications, 24(3–4), 563–571.

See Also

VMDARIMA,VMDELM

Examples

data("Data_Maize")
VMDTDNN(Data_Maize)

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