## 4 Result and Discussion
Earlier, several ensemble learning methods have been proposed in the literature for analyzing the time series data. [PERSON] et al. [19] developed an ensemble learning model with a different number of epochs using the same feedforward back-propagation neural networks (FNN) model and experimented on electricity load demand datasets such as Mackey-Glass, NSW, SA, TAS, CART, FAD and CH. They compared their proposed model's result with traditional machine learning and deep learning models such as support vector regression (SVR), feedforward neural network (FNN), deep belief network (DBN) and ensemble feedforward neural network (ENN). The RMSE results shown in Table 4 confirm that their proposed model works better than the aforementioned traditional models. However, the results they received show no significant difference. Therefore, a small difference does not create any novelty in this research area. [PERSON] et al. [22] developed a hybrid convolutional neural network for environmental sound recognition. They stacked the model with two models, MelNet [67] and RawNet [68]. Each of the models contains five convolutional neural networks and a fully connected layer. They used the accuracy evaluation metric and evaluated this model on three datasets such as ESC-10, ESC-50 and Urbansound8k, which are audio signal datasets. They achieved an accuracy of 91.4 percent for MelNet, which is 9.9 percent and 4.5 percent higher than the accuracy (81.5 percent) of previous work [PERSON]'s [69]. Finally, they evaluated the algorithms on the Urbansound8k dataset. The accuracy of MelNet is 90.2 percent, which is also higher than the 73.7 percent accuracy of [PERSON] [69] and the 79 percent of [PERSON] and [PERSON] [70]. The concept of their developed model is similar but not identical to ours. The evaluation metric and dataset differ significantly from the regression problem metric and time series dataset. [PERSON] et al. [20] developed a stacking ensemble learning with hundreds of deep learning decisions, provided by a large number of CNNs trained with historical market \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset Name** & **SVR** & **FNN** & **DBN** & **ENN** & **Proposed** \\ \hline Mackey-Glass & 0.0024 & 0.002 & 0.0018 & 0.0226 & **0.0015** \\ \hline NSW & 74.3053 & 95.8105 & 90.2061 & 78.6394 & **72.2545** \\ \hline SA & 44.6742 & 38.8585 & 35.9375 & 34.9473 & **30.5989** \\ \hline TAS & 20.1068 & 19.7952 & 19.9187 & 19.9034 & **19.7580** \\ \hline CART & 0.0406 & 0.0420 & 0.0412 & 0.0428 & **0.0403** \\ \hline FAD & 0.0339 & 0.0349 & 0.0320 & 0.0315 & **0.0313** \\ \hline CH & 0.1637 & 0.1803 & 0.1773 & 0.1615 & **0.1508** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RMSE Results derived from existing work. \begin{table}
\begin{tabular}{c l c c} \hline \hline
**Dataset** & **Machine Learning Models** & **RMSE** & **MAE** \\ \hline JAMUNABANK & ML ensemble method (RandomForest) & 0.3725 & 0.2716 \\  & ML ensemble method (AdaBoost) & 0.4013 & 0.2725 \\  & ML ensemble method (GradientBoosting) & 0.5965 & 0.4986 \\  & ML Stacking Ensemble Learning & 0.5364 & 0.4164 \\ \hline KEYACOSMET & ML ensemble method (RandomForest) & 6.7334 & 6.4327 \\  & ML ensemble method (AdaBoost) & 7.7225 & 7.4159 \\  & ML ensemble method (GradientBoosting) & 8.1252 & 7.5307 \\  & ML Stacking Ensemble Learning & 9.0140 & 8.8783 \\ \hline UTIRABANK & ML ensemble method (RandomForest) & 0.9987 & 0.6482 \\  & ML ensemble method (AdaBoost) & 1.0256 & 0.6621 \\  & ML ensemble method (GradientBoosting) & 1.0327 & 0.6583 \\  & ML stacking Ensemble Learning & 0.9832 & 0.7307 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
Figure 4: Comparison between original and predicted volatility obtained from jupyter notebook using all models and methods of the last 30 days **(a)** Of company ABBANK. **(b)** Of company ACIBANK. **(c)** Of company APEXFOOT. **(d)** Of company BANKASIA. **(e)** Of company BATASHOE. **(f)** Of company BERGERPBL. **(g)** Of company BEXIMCO. **(h)** Of company BRACBANK. **(i)** Of company CITYBANK.