# Walking Back the Data Quantity Assumption to Improve Time Series Prediction in Deep Learning
## 5 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Metrics GOLD**} \\ \hline
**Time Serie** & **ANN** & **RMSE** & **MSE** & **MAPE** & **R2** & **Time** \\ \hline \multirow{5}{*}{**TRANS**} & \(1^{\circ}\) & 3.69 & 13.64 & 1101.49 & \(-\)169.90 & **31.65** \\ \cline{2-7}  & \(2^{\circ}\) & 0.23 & 0.05 & 59.72 & 0.48 & 34.93 \\ \cline{2-7}  & \(3^{\circ}\) & **0.19** & **0.03** & **31.23** & **0.76** & 40.77 \\ \cline{2-7}  & \(4^{\circ}\) & 0.30 & 0.09 & 31.93 & 0.59 & 54.46 \\ \cline{2-7}  & \(5^{\circ}\) & **0.24** & 0.05 & 32.90 & 0.75 & 60.89 \\ \cline{2-7}  & \(6^{\circ}\) & 0.29 & 0.08 & 47.88 & 0.70 & 61.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Metrics Apple**} \\ \hline
**Time Serie** & **ANN** & **RMSE** & **MSE** & **MAPE** & **R2** & **Time** \\ \hline \multirow{5}{*}{**MLP**} & \(1^{\circ}\) & 2.75 & 7.55 & 13.41 & 0.98 & **9.22** \\ \cline{2-7}  & \(2^{\circ}\) & 3.08 & 9.51 & 12.34 & 0.97 & 10.49 \\ \cline{2-7}  & \(3^{\circ}\) & 3.87 & 14.97 & **12.31** & 0.97 & 11.34 \\ \cline{2-7}  & \(4^{\circ}\) & 2.82 & 7.97 & 14.72 & 0.98 & 13.45 \\ \cline{2-7}  & \(5^{\circ}\) & 3.55 & 12.60 & 16.64 & 0.98 & 15.65 \\ \cline{2-7}  & \(6^{\circ}\) & **2.72** \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Time Serie**} & \multicolumn{1}{c}{**ANN**} & \multicolumn{1}{c}{**RMSE**} & \multicolumn{1}{c}{**MSE**} & \multicolumn{1}{c}{**R2**} & \multicolumn{1}{c}{**Time**} \\ \hline \multirow{6}{*}{MLP} & \(1^{\circ}\) & **10.71** & **114.73** & 0.84 & **35.81** \\ \cline{2-6}  & \(2^{\circ}\) & 11.02 & 121.55 & 0.86 & 64.83 \\ \cline{2-6}  & \(3^{\circ}\) & 12.02 & 144.36 & 0.85 & 61.99 \\ \cline{2-6}  & \(4^{\circ}\) & 12.95 & 167.58 & 0.85 & 59.95 \\ \cline{2-6}  & \(5^{\circ}\) & 12.71 & 161.47 & 0.87 & 70.77 \\ \cline{2-6}  & \(6^{\circ}\) & 12.91 & 166.65 & **0.88** & 71.21 \\ \hline \multirow{6}{*}{LSTM} & \(1^{\circ}\) & **16.54** & **273.87** & 0.63 & **254.31** \\ \cline{2-6}  & \(2^{\circ}\) & 16.85 & 284.11 & 0.67 & 267.75 \\ \cline{2-6}  & \(3^{\circ}\) & 18.62 & 346.75 & 0.64 & 308.86 \\ \cline{2-6}  & \(4^{\circ}\) & 19.78 & 391.53 & 0.66 & 413.56 \\ \cline{2-6}  & \(5^{\circ}\) & 19.02 & 361.81 & **0.71** & 422.07 \\ \cline{2-6}  & \(6^{\circ}\) & 20.35 & 414.43 & 0.71 & 436.84 \\ \hline \multirow{6}{*}{TRANS} & \(1^{\circ}\) & **16.71** & **279.28** & 0.63 & **686.82** \\ \cline{2-6}  & \(2^{\circ}\) & 17.19 & 295.69 & 0.65 & 829.67 \\ \cline{2-6}  & \(3^{\circ}\) & 18.52 & 343.01 & 0.64 & 880.48 \\ \cline{2-6}  & \(4^{\circ}\) & 19.64 & 385.83 & 0.66 & 997.92 \\ \cline{2-6}  & \(5^{\circ}\) & 18.73 & 351.08 & **0.72** & 1130.10 \\ \cline{2-6}  & \(6^{\circ}\) & 21.47 & 461.04 & 0.67 & 1150.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: OMIE results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Time Serie**} & \multicolumn{1}{c}{**ANN**} & \multicolumn{1}{c}{**RMSE**} & \multicolumn{1}{c}{**MSE**} & \multicolumn{1}{c}{**MAPE**} & \multicolumn{1}{c}{**R2**} & \multicolumn{1}{c}{**Time**} \\ \hline \multirow{6}{*}{LSTM} & \(1^{\circ}\) & **11.08** & **122.89** & **5.20** & **0.71** & **42.09** \\ \cline{2-6}  & \(2^{\circ}\) & 17.81 & 317.18 & 9.15 & 0.00 & 44.47 \\ \cline{2-6}  & \(3^{\circ}\) & 27.19 & 739.29 & 13.75 & \(-1.44\) & 61.23 \\ \cline{2-6}  & \(4^{\circ}\) & 28.99 & 840.89 & 14.47 & \(-1.31\) & 70.45 \\ \cline{2-6}  & \(5^{\circ}\) & 36.04 & 1299.15 & 19.15 & \(-1.90\) & 67.74 \\ \cline{2-6}  & \(6^{\circ}\) & 75.27 & 5666.90 & 45.01 & \(-8.00\) & 80.35 \\ \hline \multirow{6}{*}{TRANS} & \(1^{\circ}\) & **38.86** & **1510.48** & **18.79** & \(-\)The GOLD time series (Table 3), being the one that consists of the fewest observations, shows that in the first iterations, it already obtains the most accurate results and with the least error.