# Walking Back the Data Quantity Assumption to Improve Time Series Prediction in Deep Learning
## 5 Experimental Results  
Tables 3-6 show the results of the methodology applied to the four data series considered in the study. The results illustrate how the last dataset, with the total number of observations, is not the one that achieves the best results on all occasions. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{\
\begin{tabular}{c} **Learning** \\ **Rate** \\ \end{tabular} } & \multirow{2}{*}{**Batch**} & \multirow{2}{*}{**Epoch**} &
\begin{tabular}{c} **Hidden** \\ **Layers** \\ \end{tabular} & \multirow{2}{*}{**Optimizer**} & \multirow{2}{*}{**Ff\_dim**} & \multirow{2}{*}{**Num\_heads**} \\ \hline
**Transformer** & 0.001 & 150 & 150 & 1 & [PERSON] & 75 & 6 \\ \hline
**LSTM** & 0.001 & 25 & 100 & 2 & [PERSON] & - & - \\ \hline
**MLP** & 0.001 & 25 & 75 & 2 & [PERSON] & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters selected. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Time Serie**} & \multicolumn{5}{c}{**Metrics GOLD**} \\ \hline
**Time Serie** & **ANN** & **RMSE** & **MSE** & **MAPE** & **R2** & **Time** \\ \hline \multirow{8}{*}{**MLP**} & \(1^{\circ}\) & 0.08 & 0.01 & **15.47** & 0.95 & 6.37 \\ \cline{2-6}  & \(2^{\circ}\) & **0.06** & **0.00** & 18.36 & **0.97** & 6.86 \\ \cline{2-6}  & \(3^{\circ}\) & 0.07 & 0.01 & 20.09 & 0.96 & **6.27** \\ \cline{2-6}  & \(4^{\circ}\) & 0.07 & 0.00 & 21.67 & 0.97 & 7.86 \\ \cline{2-6}  & \(5^{\circ}\) & 0.07 & 0.00 & 24.14 & 0.97 & 7.09 \\ \cline{2-6}  & \(6^{\circ}\) & 0.10 & 0.01 & 26.47 & 0.96 & 8.96 \\ \hline \multirow{8}{*}{**LSTM**} & \(1^{\circ}\) & **0.15** & **0.02** & 32.74 & 0.80 & **26.07** \\ \cline{2-6}  & \(2^{\circ}\) & 0.16 & 0.02 & 34.13 & 0.79 & 32.11 \\ \cline{2-6}  & \(3^{\circ}\) & 0.18 & 0.03 & 27.21 & 0.84 & 30.14 \\ \cline{2-6}  & \(4^{\circ}\) & 0.18 & 0.03 & 25.06 & 0.85 & 32.36 \\ \cline{2-6}  & \(5^{\circ}\) & 0.18 & 0.03 & **26.00** & 0.87 & 34.34 \\ \cline{2-6}  & \(6^{\circ}\) & 0.18 & 0.03 & **17.82** & **0.91** & 40.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: GOLD results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Metrics WTI**} \\ \hline
**Time Serie** & **ANN** & **RMSE** & **MSE** & **MAPE** & **R2** & **Time** \\ \hline \multirow{5}{*}{**MLP**} & \(1^{\circ}\) & 2.49 & 6.20 & 41.58 & 0.97 & 16.62 \\ \cline{2-7}  & \(2^{\circ}\) & 2.34 & 5.45 & 37.21 & 0.97 & **16.47** \\ \cline{2-7}  & \(3^{\circ}\) & 2.30 & 5.28 & 35.03 & 0.98 & 17.66 \\ \cline{2-7}  & \(4^{\circ}\) & 2.17 & 4.72 & 34.63 & 0.98 & 20.33 \\ \cline{2-7}  & \(5^{\circ}\) & 2.02 & 4.07 & **35.06** & 0.98 & 29.40 \\ \cline{2-7}  & \(6^{\circ}\) & **2.01** & **4.02** & 35.63 & **0.98** & 31.85 \\ \hline \multirow{5}{*}{**LSTM**} & \(1^{\circ}\) & 5.35 & 28.69 & 43.11 & 0.95 & **70.63** \\ \cline{2-7}  & \(2^{\circ}\) & 4.56 & 20.81 & 8.89 & 0.94 & 72.87 \\ \cline{2-7}  & \(3^{\circ}\) & 4.37 & 19.17 & 8.47 & 0.94 & 110.60 \\ \cline{2-7}  & \(4^{\circ}\) & 4.04 & 16.33 & **7.51** & 0.95 & 118.62 \\ \cline{2-7}  & \(5^{\circ}\) & **4.03** & **16.29** & 7.88 & **0.95** & 131.19 \\ \cline{2-7}  & \(6^{\circ}\) & 4.15 & 17.29 & 8.05 & 0.94 & 151.46 \\ \hline \multirow{5}{*}{**TRANS**} & \(1^{\circ}\) & 6.87 & 47.32 & 45.97 & 0.92 & **138.38** \\ \cline{2-7}  & \(2^{\circ}\) & 5.19 & 27.02 & 9.80 & 0.93 & 217.77 \\ \cline{2-7}  & \(3^{\circ}\) & 5.16 & 26.69 & 9.87 & 0.92 & 216.02 \\ \cline{2-7}  & \(4^{\circ}\) & 4.78 & 22.90 & 9.16 & 0.93 & 223.83 \\ \cline{2-7}  & \(5^{\circ}\) & 5.00 & 25.00 & 9.51 & 0.92 & 264.62 \\ \cline{2-7}  & \(6^{\circ}\) & **4.60** & **21.19** & **8.61** & **0.93** & 300.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: WTI results.