## 5 Discussion  
In order to verify the effectiveness of the proposed hybrid framework, several experiments on various factors were examined. The observation results are as follows:  
* The EMD-LSTM model outperforms state-of-the-art benchmark models indicating that decomposition methods with EMD decrease the complexity of sequences and develop prediction performance. Moreover, EMD decomposed the original signal into  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Input features** & **MAE** & **RMSE** & **MAPE** & \(\mathbf{R^{2}}\) \\ \hline PCA-EMD-LSTM & Closing price + PC 1  & 3.0086 & 3.6896 & 0.3102 & 0.9817 \\ PCA-EEMD-LSTM & Closing price + PC 1  & 3.3064 & 4.0338 & 0.3410 & 0.9782 \\ PCA-CEEMDAN-LSTM & Closing price + PC 1  & 3.4806 & 4.2967 & 0.3574 & 0.9752 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results of the prediction models with different advanced versions of EMD.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MAE** & **RMSE** & **MAPE** & \(\mathbf{R^{2}}\) \\ \hline EMD-LSTM & 3.1047 & 3.7832 & 0.3197 & 0.9808 \\ TIs-EMD-LSTM 1  & 4.5122 & 5.6629 & 0.4636 & 0.9569 \\ PCA-EMD-LSTM & 3.0086 & 3.6896 & 0.3102 & 0.9817 \\ PCA-EMD-LSTM & 3.0086 & 3.6896 & 0.3102 & 0.9817 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results of EMD-LSTM with additional input features.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MAE** & **RMSE** & **MAPE** & \(\mathbf{R^{2}}\) \\ \hline LSTM & 9.0636 & 7.0307 & 0.7255 & 0.8896 \\ LSTM with News & 7.6459 & 5.7050 & 0.5842 & 0.8127 \\ PCA-EMD-LSTM & 3.0086 & 3.6896 & 0.3102 & 0.9817 \\ PCA-EMD-LSTM with News & 5.0747 & 4.1863 & 0.4310 & 0.9654 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results of the prediction models using news sentiment.  
minor components based on their frequencies. In order to maximize the prediction effectiveness, the LSTM is trained individually by each component; therefore, the hyper-parameters and weights are different for each component. This is the significant difference that makes a hybrid EMD-LSTM model perform better than a single LSTM.
* The prediction result shows that PCA can help to reduce prediction errors in the first few IMFs when applying the principal components from PCA to the EMD-LSTM. This indicates that the PCA method creates useful features from technical indicators for improving IMF with high-frequency prediction. From the obtained results in Table 10, the PCA-EMD-LSTM achieves the highest prediction performance for the closing price of the stock market. However, based on the experimental results in Table 5, the MAPE value of the EMD-LSTM model is slightly higher than the obtained MAPE result of the PCA-EMD-LSTM. Therefore, further experiments on different datasets are required to verify the performance improvement of using PCA in the EMD-LSTM model.
* Applying the news sentiment score to the EMD-LSTM does not improve prediction results in every IMF. On the other hand, adding the news sentiment score can improve the original LSTM performance. This means that news sentiment can be used to predict the closing price of the stock market while it cannot be used to predict the decomposed component of a closing price of the stock market.  
To increase the efficiency of this proposed framework, there are a number of gaps that need further development. For example, IMFs may be adaptively predicted by various traditional or hybrid machine learning models. Recently, a novel approach [52] to select effective machine learning model combination for time series forecasting was proposed. Based on the machine learning combination approach, it can be applied to this proposed framework for improving the prediction results of each IMF and the prediction of the closing price of the stock market. In addition, based on a recently published re-search study [53], an interesting decomposition method, i.e., a hybrid time series decom-position strategy (HTD), can be applied instead of EMD for further improvement of this proposed framework.