## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **R\({}^{2}\)** & **MAE** & **MSE** & **RMS** & **Duration** \\ \hline \multicolumn{6}{c}{Test 1} \\ \hline FastForestRegression & 0.2649 & 0.34 & 0.17 & 0.41 & 2.7 \\ FastTreeRegression & 0.3037 & 0.31 & 0.16 & 0.39 & 2.6 \\ FastTreeTweedieRegression & 0.2731 & 0.33 & 0.17 & 0.40 & 2.7 \\ LbfgsPoissonRegressionRegression & \(-\)0.0470 & 0.49 & 0.25 & 0.50 & 0.2 \\ LightGbmRegression & 0.2950 & 0.34 & 0.17 & 0.40 & 0.2 \\ SdcaRegression & \(-\)0.0578 & 0.50 & 0.25 & 0.50 & 0.5 \\ \hline \multicolumn{6}{c}{Test 2} \\ \hline FastForestRegression & 0.2268 & 0.33 & 0.18 & 0.42 & 1.2 \\ FastTreeRegression & 0.3199 & 0.31 & 0.16 & 0.40 & 1.2 \\ FastTreeTreeforeforefore & 0.3071 & 0.33 & 0.16 & 0.40 & 1.1 \\ LbfgsPoissonRegressionRegression & \(-\)0.0658 & 0.48 & 0.24 & 0.49 & 0.1 \\ LightGbmRegression & 0.3232 & 0.34 & 0.16 & 0.39 & 0.3 \\ SdcaRegression & \(-\)0.0590 & 0.48 & 0.24 & 0.49 & 0.2 \\ \hline \multicolumn{6}{c}{Test 3} \\ \hline FastForestRegression & 0.9603 & 0.03 & 0.01 & 0.08 & 1.4 \\ FastTreeRegression & 0.9605 & 0.02 & 0.01 & 0.06 & 1.5 \\ FastTreeTweedieRegression & 0.9019 & 0.02 & 0.02 & 0.11 & 0.9 \\ LbfgsPoissonRegressionRegression & 0.5870 & 0.22 & 0.10 & 0.31 & 0.1 \\ LightGbmRegression & 0.9462 & 0.03 & 0.01 & 0.08 & 0.1 \\ SdcaRegression & \(-\)0.1131 & 0.46 & 0.26 & 0.51 & 0.2 \\ \hline \multicolumn{6}{c}{Test 4} \\ \hline FastForestRegression & 0.9741 & 0.02 & 0.00 & 0.05 & 10.9 \\ FastTreeRegression & 0.9417 & 0.03 & 0.01 & 0.09 & 1.1 \\ FastTreeTweedieRegression & 0.9450 & 0.02 & 0.01 & 0.08 & 1.2 \\ LbfgsPoissonRegressionRegression & 0.5293 & 0.18 & 0.09 & 0.30 & 0.1 \\ LightGbmRegression & 0.9603 & 0.03 & 0.01 & 0.07 & 0.1 \\ SdcaRegression & 0.7662 & 0.16 & 0.05 & 0.21 & 6.6 \\ \hline \multicolumn{6}{c}{Test 5} \\ \hline FastForestRegression & 0.9962 & 0.00 & 0.00 & 0.01 & 0.2 \\ FastTreeRegression & 0.9990 & 0.00 & 0.00 & 0.00 & 0.2 \\ FastTreeTweedieRegression & 0.8298 & 0.00 & 0.00 & 0.05 & 0.3 \\ LbfgsPoissonRegressionRegression & 0.0297 & 0.04 & 0.02 & 0.13 & 0.1 \\ LightGbmRegression & 0.9993 & 0.00 & 0.00 & 0.00 & 0.1 \\ SdcaRegression & 0.0132 & 0.04 & 0.02 & 0.13 & 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of analyzed algorithms by MLNET under different conditions and maintaining training time at 600 s.  
values above 0.96. This indicates that the variability range captures information about the data's extremes;
* Test 4 (15-Day Range) focuses on variability over a shorter 15-day period and yielded even higher accuracy, with FastForestRegression achieving an R\({}^{2}\) of 0.9741 and FastTreeTweedieRegression reaching 0.9450. This test showed that short-term variability, rather than averages, is an indicator for prediction, likely due to its ability to reflect recent data shifts more effectively;
* Test 5 (Daily Data), which uses all daily parameters, yielded the best overall results, with LightGbmRegression achieving an R\({}^{2}\) of 0.9993 and FastTreeRegression reaching 0.9990, alongside negligible error values. This test confirms that daily data granularity provides the most comprehensive insight, capturing daily fluctuations for the model's high accuracy and minimal prediction errors. These results indicate that using daily data or recent variability (Test 5 and Test 4) is far more effective than averaging data over time. The highest accuracy is obtained when all daily parameters are included, demonstrating that fine-grained, day-to-day data achieve optimal performance in the EMM model. The results in Table 1 demonstrate the superiority of the EMM model when training data are used for the daily parameter values. The performance of the LightGbmRegression-model trained on daily data for 600 s shows superior results compared to the other tests. An R\({}^{2}\) of 0.9993 indicates a good fit. The MAE (0.00), MSE (0.00), and RMS (0.00) suggest minimal prediction errors.