## 6 Conclusions  
For data scientists and practitioners, predicting the stock market indices is a fascinating and difficult area of research. Precise forecasting of the stock market is of prime importance as it will help not only the investors to maximize their profits, but the governments as well because the stock markets are the backbone of every country's economy. This section summarizes and presents the major findings of this research study. In the previous literature, the researchers employed a single time series or machine-learning model to predict the accurate values of the stock prices and hence, were criticized for their low prediction ability. The primary goal of this study is to suggest a unique approach for effectively and correctly forecasting the daily closing prices of the KSE-100 index. The fact that we presented a hybrid technique that combines the best features of both the Akima-EMD and the LSTM network. As a result, the proposed approach is extremely effective for prediction with nonlinear and nonstationary data. The suggested model is not an ensemble model since we used subcomponents after decomposing the data into various IMFs and a single monotone residue using the Akima-EMD approach. The Pearson coefficient of correlation was utilized to find out which IMFs (residue) is correlated with the actual data and then construct the proposed hybrid model. Comparison is made with other ensemble models that are based on other machine learning techniques such as SVM, random forest, and decision tree. The findings of four statistical measures, namely RMSE, MAE, and MAPE, show that the suggested model surpassed the other models, implying that it is a useful addition to stock market prediction. Interestingly, the hybrid ensemble SVM model stands second in this competition and outperformed the individual LSTM, random ensemble forest, and decision tree models even though the single LSTM model, which is a type of RNN, performs better than all the ensemble machine learning models except the proposed one. In future work, the proposed method can be used for other countries' stock markets data, and comparisons could be made with XGBoost, SVM, and ANN. Whereas the SVM and ANN models can be constructed using different variables such as investor sentiments, interest rates, the political climate of the country, news, and exchange rates as inputs. Furthermore, we will also find out the prediction accuracy of our proposed model on other nonlinear and non-stationary time series, such as exchange rates, crude oil prices, wind speed, temperature, rainfall, earthquakes, and tourist arrival. However, for the current research work, we proved that the proposed hybrid Akima-EMD-LSTM model performs better in predicting the daily closing price of the KSE-100 index. Conceptualization, D.M.K. and M.A. ; methodology, D.M.K. and H.M.A. ; software, M.A. and D.M.K. ; validation, H.M.A. and A.A.-A.H.E.-B. ; formal analysis, M.A. and D.M.K. ; investigation, H.M.A. and A.A.-A.H.E.-B. ; resources, M.A. and D.M.K. ; data curation, D.M.K. ; writing--original draft preparation, M.A. ; writing--review and editing, M.A. and D.M.K. ; \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **RMSE** & **MAE** & **MAPE** \\ \hline LSTM & 473.319 & 354.75 & 0.87 \\ EMD-SVM & 317.504 & 246.675 & 0.711 \\ EEMD-SVM & 322.675 & 280.937 & 0.65 \\ SEMD-SVM & 324.494 & 285.268 & 0.724 \\ Akima-EMD-SVM & 309.345 & 242.879 & 0.726 \\ EMD-Random Forest & 406.554 & 289.891 & 0.748 \\ EEMD-Random Forest & 389.023 & 253.851 & 0.65 \\ SEMD-Random Forest & 521.028 & 338.445 & 1.611 \\ Akima-EMD-Random Forest & 367.987 & 256.987 & 0.712 \\ EMD-Decision Tree & 480.217 & 352.165 & 0.896 \\ EEMD-Decision Tree & 742.619 & 411.442 & 1.067 \\ SEMD-Decision Tree & 630.078 & 377.376 & 0.973 \\ Akima-EMD-Decision Tree & 443.91 & 345.1 & 0.709 \\ Hybrid Akima-EMD-LSTM & 299.541 & 234.34 & 0.578 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Performance metrics of different hybrid models. visualization, H.M.A. and A.A.-A.-H.E.-B. ; supervision, D.M.K.