## 4 Experimental Methods and Results
### Experimental Methods and Results of the Proposed Framework
#### 4.3.1 Comparisons Result between the EMD-LSTM Model and Other Models  
In this experiment, machine learning methods and the original closing price are applied to estimate the prediction performance. The EMD-LSTM, LSTM and ARIMA are used for comparison. Table 5 shows that the EMD method has great advantages in the closing price of stock market prediction, with MAE dropping by 56.13% when compared to LSTM and 85.67% when compared to the ARIMA model. Moreover, LSTM and ARIMA had a close model performance. This implies that a single model cannot impressively solve data patterns and make brilliant predictions. In addition, Figure 8 shows the predictive results of the LSTM and EMD-LSTM, revealing that the predicted values of the EMD-LSTM series visibly deviate from the original data. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiment** & **Model Name** & **Input Features** \\ \hline \multirow{3}{*}{1} & LSTM & Closing price \\  & ARIMA & Closing price \\  & EMD-LSTM & Closing price \\ \hline \multirow{3}{*}{2} & TIs-EMD-LSTM 1  & Closing price + technical indicators \\  & PCA-EMD-LSTM & Closing price + PC 2  \\ \hline \multirow{3}{*}{3} & LSTM with News & Closing price + News \\  & PCA-EMD-LSTM & Closing price + PC 2  \\  & PCA-EMD-LSTM with News & Closing price + PC 2  \\  & PCA-EMD-LSTM with News & Closing price + PC 2 [FOOTNOTE:2]Footnote 2: TC is principal components from PCA. 4.3.2. Comparisons of the Effects of Adding Principal Component and Technical Indicator to EMD-LSTM  
From the previous experiment, the EMD-LSTM model outperforms when compared with the other prediction models. This experiment applied an additional input feature to the proposed model, which is the original technical indicator and the principal components from PCA. In order to make predictions with the EMD-LSTM model, individual IMFs were predicted with LSTM and the additional input feature. After tuning of the LSTM model, the optimal hyper-parameters were obtained to achieve the prime prediction results for the IMFs, as shown in Table 6. The batch size was between 8 and 32 while the epoch was between 150 and 300. In addition, the other settings of the LSTM model included hyperbolic tangent as activation function, ADAM as optimizer, mean squared error as loss function and learning with 0.001. Figure 8: Results of stock price prediction from LSTM (**a**) and LSTM-EMD (**b**). The experimental results of adding the input feature of each IMF are shown in Table 7. The results show that the principal components can improve the efficiency of the model and outperform the IMF and technical indicators in the first four IMFs. Nevertheless, after the fifth IMF, the model that uses only the IMF value outperforms the other models using additional input features, as can be seen in Figure 9. Meanwhile, models with a technical indicator as an input feature perform the worst across all IMFs. In addition, Figure 10 shows the IMF of closing price testing set prediction results. Due to the high frequency of the components, the prediction values of the first several IMF components explicitly diverge from the original data, but the prediction values of the last IMF nearly matched the original data. Next, the prediction results of each IMF are assembled in order to compare the final closing price prediction results. In addition, the PCA-EMD-LSTM model used principal components as an input feature to predict IMF 1 to 4 but uses only the IMF value for IMF 5 to 7. From Table 8, the result shows that PCA-EMD-LSTM achieves the best prediction result, followed by a close second to the model that uses only the IMF value, whereas models using technical indicators as the input feature have the worst prediction results. \begin{table}
\begin{tabular}{c c c} \hline \hline
**IMFs** & **Batch Size** & **Epoch** \\ \hline
1 & 8 & 300 \\
2 & 8 & 250 \\
3 & 32 & 200 \\
4 & 16 & 180 \\
5 & 16 & 200 \\
6 & 16 & 150 \\
7 & 32 & 150 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Hyper-parameters of LSTM.