# A Novel Approach to Predict the Asian Exchange Stock Market Index Using Artificial Intelligence
## 3 Dataset
### Results and Discussion
Moreover, upon careful examination of Figure 11, it is evident that the loss value tends to reach a state of stability around the 91 st iteration (epoch). Also, Figure 11b and f show that the Conv. LSTM and Conv. BGRU models have the fastest loss and value reduction, which shows that they are stable, especially when compared to the Conv. RNN and Conv. LSTM models. Conversely, Conv. RNN exhibits the slowest rate of achieving stability. Figure 9: Training and value loss of all six Asian indices with BGRU model training. (**a**) Training and value loss for BSE index with BGRU Model; (**b**) training and value loss for HSI index with BGRU Model; (**c**) training and value loss for TWII index with BGRU Model; (**d**) training and value loss for JKSE index with BGRU Model; (**e**) training and value loss for JPXGY index with BGRU Model; (**f**) training and value loss for KS11 index with BGRU Model. Further, the six assessed deep learning models successfully replicate the observed patterns of index value fluctuations in the six stock markets shown in Figure 12a-f. On the other hand, when compared to the initial value curve, the predicted yield curve demonstrates a considerable divergence. The Convolutional RNN model generates the BSE index curve that displays the most substantial deviation from the initial value curve compared to the additional models. The Convolutional BGRU model, on the other hand, generates the BSE prediction curve, which is comparable to the value curve that is first determined in Figure 12a. Figure 12 displays various possibilities on the BSE stock market chart. The prediction curve of Convolutional LSTM shows the most departure from the beginning value on this graph compared to the curves of the other models. The Convolutional Bi-directional RNN model accurately creates the prediction curve for the BSE as shown in Figure 12d. In addition, the Convolutional BGRU model has the highest  
Figure 10: Actual and prediction values of all six Asian indices with BGRU model. **(a)** Actual and prediction values for BSE index with BGRU model; **(b)** actual and prediction values for HSI index with BGRU model; **(c)** actual and prediction values for TWII index with BGRU model; **(d)** actual and prediction values for JKSE index with BGRU model; **(e)** actual and prediction values for JKSY index with BGRU model; **(f)** actual and prediction values for KS11 index with BGRU model. level of accuracy in predicting the curve for BSE. In contrast, the Convolutional BLSTM model remains the top performer in performance for BSE. The statistics presented here demonstrate that none of the six investigated deep learning models achieve the highest accuracy regarding the generation of predictions. Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) are the metrics that are utilized to measure the disparities that exist between the values that were anticipated and those that were observed. Tables 3 and 4 illustrate these distinctions in a graphical format. The fact that the error rates for RMSE and MAPE are comparable is illustrated by this, which shows that standalone models can deliver findings that are superior to those produced by combining models. Table 3, which can be located elsewhere, presents a comparison of the error rates that are observed during the training and testing stages of the process. During the training phase of the calculation, it is not unusual for the error rates to increase when dealing with time-series prediction. This is because of the nature of the problem. The fact that this particular case exists proves that this pattern does exist. The Convolutional LSTM algorithm has a value of 0.1404 in  
Figure 11: Training and Value Loss for BSE index with BGRU, Conv. LSTM, Conv. GRU, Conv. RNN, Conv. BLSTM, and Conv. BGRU models. (**a**) Training and value loss for BSE index with Conv. RNN; (**b**) training and value loss for BSE index with Conv. LSTM; (**c**) training and value loss for BSE index with Conv.