## 2 Materials and Methods
### Dataset Description
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Dataset Variable** & **Data Points** & **Mean Value** & \(\sigma\) & **Minimum Value** & **Maximum Value** & **GPSR Variable Symbol** \\ \hline PBstart & \(-2.484\times 10^{-6}\) & \(5.346\times 10^{-6}\) & \(-1.800\times 10^{-5}\) & \(1.613\times 10^{-5}\) & \(X_{0}\) \\ \hline PCstart & \(2.491\times 10^{-5}\) & \(2.596\times 10^{-5}\) & \(-2.160\times 10^{-5}\) & \(6.359\times 10^{-5}\) & \(X_{1}\) \\ \hline PDstart & \(-1.316\times 10^{-5}\) & \(1.014\times 10^{-5}\) & \(-2.990\times 10^{-5}\) & \(5.149\times 10^{-5}\) & \(X_{2}\) \\ \hline PFstart & \(-5.240\times 10^{-6}\) & \(2.580\times 10^{-5}\) & \(-3.520\times 10^{-5}\) & \(4.211\times 10^{-5}\) & \(X_{3}\) \\ \hline PArise & \(1.710\times 10^{-5}\) & \(4.501\times 10^{-6}\) & \(8.464\times 10^{-6}\) & \(2.682\times 10^{-5}\) & \(X_{4}\) \\ \hline PBrise & \(1.973\times 10^{-5}\) & \(3.843\times 10^{-6}\) & \(9.338\times 10^{-6}\) & \(3.221\times 10^{-5}\) & \(X_{5}\) \\ \hline PCrise & \(3.012\times 10^{-5}\) & \(7.718\times 10^{-6}\) & \(1.157\times 10^{-5}\) & \(5.354\times 10^{-5}\) & \(X_{6}\) \\ \hline PDrise & \(1.298\times 10^{-5}\) & \(2.078\times 10^{-6}\) & \(8.705\times 10^{-6}\) & \(3.444\times 10^{-5}\) & \(X_{7}\) \\ \hline PFrise & \(1.675\times 10^{-5}\) & \(7.026\times 10^{-6}\) & \(8.060\times 10^{-6}\) & \(3.158\times 10^{-5}\) & \(X_{8}\) \\ \hline PAfall & \(2.148\times 10^{-5}\) & \(1.826\times 10^{-5}\) & \(1.521\times 10^{-5}\) & \(6.814\times 10^{-5}\) & \(X_{9}\) \\ \hline PBfall & \(2.516\times 10^{-5}\) & \(3.781\times 10^{-5}\) & \(1.596\times 10^{-5}\) & \(9.416\times 10^{-5}\) & \(X_{10}\) \\ \hline PCfall & \(2.712\times 10^{-5}\) & \(4.878\times 10^{-5}\) & \(1.388\times 10^{-5}\) & \(9.884\times 10^{-5}\) & \(X_{11}\) \\ \hline PDfall & \(2.588\times 10^{-5}\) & \(4.621\times 10^{-5}\) & \(1.415\times 10^{-5}\) & \(9.806\times 10^{-5}\) & \(X_{12}\) \\ \hline PFfall & \(2.472\times 10^{-5}\) & \(5.361\times 10^{-5}\) & \(1.163\times 10^{-5}\) & \(9.355\times 10^{-5}\) & \(X_{13}\) \\ \hline PAwidth & \(1.696\times 10^{-5}\) & \(3.659\times 10^{-5}\) & \(7.105\times 10^{-5}\) & \(2.271\times 10^{-5}\) & \(X_{14}\) \\ \hline PBwidth & \(2.071\times 10^{-5}\) & \(3.343\times 10^{-5}\) & \(7.133\times 10^{-5}\) & \(2.983\times 10^{-5}\) & \(X_{15}\) \\ \hline PCwidth & \(2.467\times 10^{-5}\) & \(2.453\times 10^{-5}\) & \(1.648\times 10^{-5}\) & \(3.443\times 10^{-5}\) & \(X_{16}\) \\ \hline PDwidth & \(1.494\times 10^{-5}\) & \(5.623\times 10^{-5}\) & \(3.603\times 10^{-5}\) & \(3.033\times 10^{-5}\) & \(X_{17}\) \\ \hline PFwidth & \(1.765\times 10^{-5}\) & \(7.636\times 10^{-5}\) & \(3.321\times 10^{-5}\) & \(2.665\times 10^{-5}\) & \(X_{18}\) \\ \hline y & \(-21.758\) & \(14.091\) & \(-41.9\) & \(0\) & \(y\) \\ \hline \end{tabular}
\end{table} 
Table 2: The results of the statistical investigation of dataset variables including the GSPR variable representation. As seen from Figure 3, the highest negative correlation (\(-0.98\)--\(-0.83\)) was achieved between PCStart, PFStart, PFrise, PFwidth, and the output variable y. The highest positive correlation (0.86-0.95) was achieved between PAwidth, PArise, and output variable y, respectively. The poorest correlation coefficient was achieved in the cases of PBstart-y (\(-0.01\)), PBfall-y (\(-0.32\)), PCfall-y (\(-0.36\)), PCwidth-y (\(-0.39\)), and PCwidth-y (\(-0.32\)). From the correlation heat map, it can be noticed that PBfall, PCfall, PDfall, and PFfall are mutually highly correlated variables, while showing a poor correlation in regard to the output. However, since GPSR is not a computationally intensive algorithm, it is good practice to include all input variables in the investigation. The reason why all input variables were included was to compare the variables used in the final models with their correlations.