ceemdanTDNN {eemdTDNN} R Documentation

CEEMDAN Based Time Delay Neural Network Model

Description

The ceemdanTDNN function computes forecasted value for Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise Based Time Delay Neural Network Model with different forecasting evaluation criteria.

Usage

ceemdanTDNN(data, stepahead=10,
num.IMFs=emd_num_imfs(length(data)),
s.num=4L, num.sift=50L, ensem.size=250L, noise.st=0.2)


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. ensem.size Number of copies of the input signal to use as the ensemble. noise.st Standard deviation of the Gaussian random numbers used as additional noise. This value is relative to the standard deviation of the input series.

Details

Torres et al.(2011) proposed Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). This algorithm generates a Fewer IMFs on the premise of successfully separating different components of a series, which can reduce the computational cost.

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 FinalCEEMDANTDNN_forecast  Final forecasted value of the CEEMDAN based TDNN model. It is obtained by combining the forecasted value of all individual IMF. MAE_CEEMDANTDNN  Mean Absolute Error (MAE) for CEEMDAN based TDNN model. MAPE_CEEMDANTDNN  Mean Absolute Percentage Error (MAPE) for CEEMDAN based TDNN model. rmse_CEEMDANTDNN  Root Mean Square Error (RMSE) for CEEMDAN based TDNN model.

References

Torres, M.E., Colominas, M.A., Schlotthauer, G. and Flandrin, P. (2011) A complete ensemble empirical mode decomposition with adaptive noise. In 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 4144–4147). IEEE.

Wu, Z. and Huang, N.E. (2009) Ensemble empirical mode decomposition: a noise assisted data analysis method. Advances in adaptive data analysis, 1(1), 1–41.