# Forecasting Daily Temperatures with Different Time Interval Data Using Deep Neural Networks
## 4 Data
### Experimental Results
The experimental results with the global variables, the variables from all three regions, showed better results than those with the local variables, the input variables only from the target region, for the average and maximum temperature of Daegwallyeong area and the average, minimum, and maximum temperature of Seongsan area. Especially, the average MAE of the maximum temperature in Daegwallyeong area was improved from 2.461 to 2.370, and the average MAEs of average and maximum temperature in Seongsan area were improved from 1.210, 1.900 to 1.110, 1.781, respectively. It can be considered that the CNN model extracted useful information from input data of other regions when forecasting a specific region. It is noteworthy that the forecasting performance is improved even for the maximum temperatures, which has high forecasting difficulty. Finally, the average MAEs with standard deviations of multitask learning are also shown in Table 21 where the best results are boldfaced. The multitask model for forecasting the average temperatures in three regions simultaneously improved the forecasting performance of Seongsan. The multitask model for maximum temperatures also improved forecasting performance in Seongsan. Finally, the multitask model, which forecasted nine temperatures simultaneously, performed better at maximum temperature in Daegwalleong and minimum and maximum temperatures in the Seongsan. The multitask learning showed better performance for tasks equal to single task with global conditions compared to single task with local conditions. Therefore, the improvement of average temperature performance in Daegwallyeong was mainly due to the effect of global conditions. However, the \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Metropolitan, Seoul**} & \multicolumn{3}{c}{**Mountainous, Daegwalleong**} & \multicolumn{3}{c}{**Coastal, Seongsan**} \\ \hline
**Temperature** & **Average** & **Minimum** & **Maximum** & **Average** & **Minimum** & **Maximum** & **Average** & **Minimum** & **Maximum** \\ \hline numerical data only & **1.558** & **1.428** & **2.281** & **1.284** & **1.654** & **1.971** & 1.309 & **1.146** & **1.629** \\ multimodal learning with satellite images & (0.106) & (0.114) & (0.107) & (0.159) & (0.201) & (0.150) & **(0.127)** & (0.113) & (0.198) \\ \hline \hline \end{tabular}
\end{table} 
Table 20: Forecasting results, represented using the MAE for multimodal learning. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Metropolitan, Seoul**} & \multicolumn{3}{c}{**Mountainous, Daegwalleong**} & \multicolumn{3}{c}{**Coastal, Seongsan**} \\ \hline
**Temperature** & **Average** & **Minimum** & **Maximum** & **Average** & **Minimum** & **Maximum** & **Average** & **Minimum** & **Maximum** \\ \hline previous & 1.308 & 1.246 & 2.053 & 1.518 & 1.700 & 2.461 & 1.210 & 1.238 & 1.900 \\ point & (0.029) & (0.041) & (0.039) & (0.046) & (0.049) & (0.073) & (0.025) & (0.035) & (0.008) \\ linear & 1.306 & 1.244 & 2.064 & 1.523 & 1.711 & 2.501 & 1.215 & 1.240 & 1.905 \\ interpolation & (0.034) & (0.021) & (0.042) & (0.033) & (0.055) & (0.237) & (0.040) & (0.017) & (0.073) \\ \(p\)-value & 0.431 & 0.391 & 0.176 & 0.310 & 0.240 & 0.214 & 0.285 & 0.428 & 0.412 \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Forecasting results, represented using the MAE for two imputation methods. maximum temperature in Daegwallyeong and all temperatures in Seongsan can be interpreted as the effect of shared information between related tasks as well as the effects of global condition. To sum up the results, hourly input data are more effective for daily temperature forecasting than daily input data. Therefore, we conclude that when forecasting daily temperatures, detailed hourly data provide better information than daily data. Here, a CNN with hourly input data outperformed an MLP and LSTM. The length of time of the input data depended on both the model and the region, and the regions showed significant differences in forecasting difficulty.