# Forecasting Daily Temperatures with Different Time Interval Data Using Deep Neural Networks
## 6 Conclusions  
We performed daily average, minimum, and maximum temperature forecasting with daily and hourly numerical weather data using neural network models. The hourly input, a more detailed time unit than the target data, had never been studied before. In order to process the detailed and abundant input data, we approached it from the perspective of two-dimensional signal processing using diverse neural network models, MLP, LSTM, and CNN. In particular, a CNN had never been used to forecast temperatures using numeric weather data. In addition, we conducted a comparison study to analyze temperature forecasting tasks affected by various factors. We found that the CNN showed better forecasting performance than did the MLP and LSTM. For time units of the input data, hourly input data worked better than daily input data in most cases. The time length of the input data depended on both the model and the region. To sum up the experimental results, depending on the target region, the CNN model using hourly input data of appropriate time length showed the best performance. In addition, using data from different regions together and multitask learning were effective in improving temperature forecasting performance in some cases. Future researchers can incorporate loss balancing algorithms for multitask learning to improve the performance of challenging tasks. To improve the performance of difficult tasks including maximum temperature, researchers could consider dynamic task prioritization [45] and weighted loss function [46]. In addition, the proposed procedure in this study, including the usage of high-frequent inputs and variables from other regions, and employment of multitask learning algorithms, can be applied to \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 single task & **1.308** & **1.246** & **2.053** & \(1.518\) & **1.700** & 2.461 & 1.210 & 1.238 & 1.900 \\ learning with & **(0.029)** & **(0.041)** & **(0.039)** & (0.046) & **(0.049)** & (0.073) & (0.025) & (0.035) & (0.080) \\ local variables & single task & 1.356 & 1.310 & 2.122 & **1.491** & 1.816 & 2.370 & 1.110 & 1.210 & 1.781 \\ learning with & (0.144) & (0.137) & (0.116) & **(0.139)** & (0.227) & (0.141) & (0.093) & (0.086) & (0.244) \\ global variables & multitask & & & & & & & & \\ learning for & 1.327 & - & - & 1.492 & - & - & **1.109** & - & - \\ average & (0.043) & - & - & (0.114) & - & - & **(0.074)** & & - \\ temperatures & multitask & & & & & & & & \\ learning for & - & (0.082) & - & - & 1.734 & - & - & 1.251 & - \\ temperatures & & & & & (0.087) & - & - & (0.155) & & \\ multitask & & & & & & & & & \\ learning for & - & - & 2.178 & - & - & 2.423 & - & - & 1.743 \\ maximum temperatures & & & (0.115) & - & - & (0.098) & - & - & (0.096) \\ temperatures & & & & & & & & & \\ multitask learning for all & 1.399 & 1.375 & 2.211 & 1.498 & 1.861 & **2.331** & 1.131 & **1.199** & **1.671** \\ temperatures & (0.102) & (0.123) & (0.094) & (0.115) & (0.140) & **(0.091)** & (0.066) & **(0.112)** & **(0.060)** \\ \hline \hline \end{tabular}
\end{table} 
Table 21: Forecasting results of, represented using the MAE for global variables and multitask learning. forecast other weather variables including amount of water precipitation, solar radiation quantity, wind speed, and so forth. S.L. responsible the whole part of the paper (Conceptualization, methodology, analysis and writing--original draft preparation) [PERSON]-S.L. responsible methodology, and review and editing and [PERSON] responsible methodology, review and editing, and supervision. All authors have read and agreed to the published version of the manuscript. The first and second authors ([PERSON] and [PERSON]-S.L.)