Modeling and Analysis of Data-Driven Systems through Computational Neuroscience Wavelet-Deep Optimized Model for Nonlinear Multicomponent Data Forecasting

Table 1

Bayesian optimization hyperparameter space.

Hyperparameters	Type	Min	Max
Wavelet decomposition layers	Integer	1	15
Mother wavelet function	Categorical	{sym2, sym7, sym12, sym18,coif1, coif5, coif10, coif15, bior1.3, bior2.6, bior3.5, bior6.8, db3, db9, db13, db18, db35, db25, rbio1.1, rbio2.6, rbio3.5, rbio5.5, rbio4.4, rbio6.8’}
No. 1 hidden units	Integer	{24,36,48}
Dropout rate	Uniform	0	0.5
Batch-size	Integer	{1, 5, 10, 15, 20, 30, 50}
Epochs	Integer	{100, 150, 200, 250, 300, 350, 400, 450, 500}
Optimizer	Categorical	{Adadelta, Adam, Sgd}