## 4 Results
#### ANOVA Test  
ANOVA tests [43] are used to objectively compare and effectively assess the performance of the proposed CAGBA with the GBAMF, LSTMMF, CALSTM, SVRMF, and CASVR techniques. ANOVA is a statistical formula used to compare variance values in the arithmetic mean of the different groups. The hypothesis of interest for ANOVA is as follows [22]:  
\[H_{0}:\Theta_{1}=\Theta_{2}...=\Theta_{j},\quad H_{1}:\Theta_{1}\
eq\Theta_{2}... \
eq\Theta_{j} \tag{5}\]  
The null hypothesis of ANOVA indicates no difference in the means between the groups, and the alternative hypothesis implied that the means between the groups were not equal. Therefore, in this study, multiple comparisons between groups are used to determine the effects of other groups and their means. One-way ANOVA is a very compact linear model, given \(e_{ij}=\alpha_{j}+e_{ij}\). Here, \(e_{ij}\) represents the experimental results (MAE) of CAGBA with GBAMF, LSTMMF, CALSTM, SVRMF, and CASVR, where \(i=20\) represents the number of test and groups \(j=6\) (number of model). We compare the MAE results between SVRMF and CASVR using the RRSYNTH dataset, as shown in Table 4. The total degree of freedom (df) is the total number of measurements (MAE) minus one: 39 (=40). The degree of freedom between groups is denoted as 1 (=\(2-1\)). In Table 4, MS shows the mean squared error (sum of squares (SS) / df = 71.66), and the F-statistic shows the ratio of the mean squared error (71.66/0.61 = 118.02). The \(p\)-value, \(3.25\times 10^{-13}\), is the computed test statistic, that is, the probability of obtaining a value greater than P(F \(>118.02\)). We also compare the MAE results of the LSTMMF and CALSTM models as shown in Table 4. Here, the \(p\)-value (0.319) shows a value greater than the significance level of (0.05). In Table 5, the MAE results of the GBAMF and CAGBA models are presented. We can see that the \(p\)-value (\(2.43\times 10^{-24}\)) between GBAMF and CAGBA models is very small compared to the significance level of (0.05). Table 5 shows df(=59) and \(p\)-value (0.316) obtained from MAE results of SVRMF, LSTMMF, and GBAMF models using the RRSYNTH dataset. We then see the \(p\)-value (\(1.42\times 10^{-27}\)) acquired from MAE results of CASVR, CALSTM, and CAGBA models using the RRSYNTH dataset and observe the \(p\)-value (\(4.69\times 10^{-45}\)) obtained using six models as in Table 6. Table 7 displays the \(p\)-value (0.805) obtained from the MAE results of the SVRMF and CASVR models on the BIDMC dataset. We can see the \(p\)-value (0.355) acquired from the the MAE results of the LSTMMF and CALSTM models on the BIDMC dataset. Table 8 shows the \(p\)-value (0.814) obtained using the MAE results of the GBAMF and CAGBA models, and we notice \(p\)-value (0.036) obtained using the MAE results of the SVRMF, LSTMMF, and GBAMF models. Table 9 shows the \(p\)-value (0.0043) calculated \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Dataset** & **Unit** & **SVRMF** & **CASVR** & **LSTMMF** & **CALSTM** & **GBAMF** & **CAGBA** \\ \hline RRSYNTH & (s) & 60.47 & 10.42 & 94.15 & 92.75 & 75.28 & 23.60 \\ BIDMC & (s) & 51.56 & 8.76 & 92.81 & 89.35 & 62.33 & 17.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Twenty total training and testing times were compared between the conventional and proposed methodology, where the specifications of the computer system are Intel®Core(TM) i5-9400 CPU 4.1 GHz, RAM 16.0 GB, OS 64 bit, and Matlab®2022 (The MathWorks Inc., Natick, MA, USA). \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Dataset** & **Errors** & **SVRMF** & **CASVR** & **LSTMMF** & **CALSTM** & **GBAMF** & **CAGBA** \\ \hline RRSYNTH & MAE & 5.57 & 2.89 & 5.63 & 5.35 & 5.25 & 1.06 \\  & SD & 0.06 & 0.15 & 0.44 & 0.16 & 0.42 & 0.41 \\ \hline BIDMC & MAE & 2.01 & 2.05 & 2.37 & 2.54 & 1.98 & 1.94 \\  & SD & 0.45 & 0.58 & 0.60 & 0.56 & 0.48 & 0.61 \\ \hline \hline \end{tabular}
\end{table}