ceemdanTDNN {eemdTDNN} | R Documentation |

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.

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

`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. |

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.

`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. |

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.

emdTDNN, EEMDTDNN

```
data("Data_Maize")
ceemdanTDNN(Data_Maize)
```

[Package *eemdTDNN* version 0.1.0 Index]