emdTDNN {eemdTDNN} R Documentation

## Empirical Mode Decomposition Based Time Delay Neural Network Model

### Description

The emdTDNN function gives forecasted value of Empirical Mode Decomposition based Time Delay Neural Network Model with different forecasting evaluation criteria.

### Usage

emdTDNN(data, stepahead=10,
num.IMFs=emd_num_imfs(length(data)),
s.num=4L, num.sift=50L)


### Arguments

 data Input univariate time series (ts) data. stepahead The forecast horizon. num.IMFs Number of Intrinsic Mode Function (IMF) for input series. s.num Integer. Use the S number stopping criterion for the EMD procedure with the given values of S. That is, iterate until the number of extrema and zero crossings in the signal differ at most by one, and stay the same for S consecutive iterations. num.sift Number of siftings to find out IMFs.

### Details

This function firstly, decompose the nonlinear and nonstationary time series into several independent intrinsic mode functions (IMFs) and one residual component (Huang et al., 1998). Secondly, time delay neural network is used to forecast these IMFs and residual component individually. Finally, the prediction results of all IMFs including residual are aggregated to form the final forecasted value for given input time series.

### Value

 TotalIMF  Total number of IMFs. 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. FinalEMDTDNN_forecast  Final forecasted value of the EMD based TDNN model. It is obtained by combining the forecasted value of all individual IMF. MAE_EMDTDNN  Mean Absolute Error (MAE) for EMD based TDNN model. MAPE_EMDTDNN  Mean Absolute Percentage Error (MAPE) for EMD based TDNN model. rmse_EMDTDNN  Root Mean Square Error (RMSE) for EMD based TDNN model.

### References

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.

Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q. and Liu, H.H. (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non stationary time series analysis. In Proceedings of the Royal Society of London A: mathematical, physical and engineering sciences. 454, 903–995.

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, 563–571.