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Among the discrete wavelet functions available within the Matlab toolbox, the four commonly used wavelet functions, namely, the Haar wavelet, the Daubechies wavelet of order 3, Biorthogonal wavelet, and the Symlets wavelet of order 4 are used for the sensitivity analysis in this study. The FFNN model and level 2 of wavelet decomposition is used to compare the forecasting performance of the four different wavelet functions. The results presented in Table 4 show that the Daubechies wavelet of order 3 produced the best forecasting performance based on the MSE and E values, whereas the Symlets wavelet of order 4 produced the best forecasting performance based on the MAPE value.

Table 4

Performance of the FFNN model on the testing dataset of Data #1 for different wavelet functions

  Performance indicators for Data #1
Wavelet functionhnMSEMAPEE
Haar wavelet 20 0.018 78.9 0.786 
Daubechies wavelet of order 3 0.016 62.4 0.802 
Biorthogonal wavelet 16 0.018 51.1 0.782 
Symlets wavelet of order 4 12 0.020 38.4 0.755 
  Performance indicators for Data #1
Wavelet functionhnMSEMAPEE
Haar wavelet 20 0.018 78.9 0.786 
Daubechies wavelet of order 3 0.016 62.4 0.802 
Biorthogonal wavelet 16 0.018 51.1 0.782 
Symlets wavelet of order 4 12 0.020 38.4 0.755 

hn, number of hidden neurons.

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