VMDTDNN {vmdTDNN}

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)