# Forecasting Daily Temperatures with Different Time Interval Data Using Deep Neural Networks
## 4 Data
### Experimental Results
The differences in daily temperature increase the forecasting difficulty because there were high daily temperature differences in the maximum temperatures in the mountainous region. In this regard, CNN using hourly input data, which showed the best results, reported interesting results as follows. In the coastal region, unlike the other regions, the CNN with hourly input data performed best with longer input data. We interpret this to reflect that a CNN extracts valid information from earlier times for forecasting temperatures in areas with relatively lower daily differences, as shown in Table 18. In contrast, in areas with larger differences, the CNN appeared to employ relevant, more recent input data. Therefore, it is necessary to set appropriate time lengths for the input data according to the daily differences in a region to improve forecasting performance. In addition, we performed a comparative experiment on the preprocessing methods of missing values by accident. In the case of the existing experiments, as mentioned in 4.2.1, missing values by accident were replaced with data from the previous point, which is one of the widely used preprocessing methods for time series data. Another typical method is linear interpolation, which interpolates missing values using data before and after the point in time. We performed forecasting temperatures using the data preprocessed in each method to compare the two preprocessing methods. The experiment was conducted with CNN using three days of hourly data that showed the best overall performance. After 25 experiments were repeated for each forecasting temperature task, we performed one-tailed t-tests and the results are shown in Table 19. The experimental results are the average MAE and standard deviation of each method and the \(p\)-value of the one-tailed t-tests. Since the smallest p-value was 0.176, the two methods did not show statistically significant difference. We interpreted this result as follows. The 10-year hourly data consists of a total of 87,648 rows and each variable have only less than 100 missing values on average. Also, the linear interpolation may not be usable in actual cases because it uses future values to impute missing values. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Temperature** & **Metropolitan, Seoul** & **Mountainous, Daegwalleong** & **Coastal, Seongsan** \\ \hline average & 1.749 & 2.075 & 1.467 \\ minimum & 1.972 & 2.854 & 1.734 \\ maximum & 2.457 & 2.764 & 1.957 \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Average of daily temperature absolute differences. The followings are the experimental results of combining the proposed method with recent approaches. All experiments on multimodal learning, global variables, and multitask learning were conducted with CNN-based models using three-day hourly inputs. First, Table 20 show the average MAEs with standard deviations for multimodal learning. The best results are boldfaced. The multimodal learning using satellite image with numerical weather data did not improve overall performance. Excluding the average temperature in the Seongsan region, the average MAEs were higher than the model using the numerical weather data. In other words, providing additional satellite image was not effective to forecast temperature. We interpreted the causes of the result as follows. First, the time interval between satellite images is so large that it could not improve the temperature forecasting performances. Utilizing higher-frequent satellite images may be helpful in this case. Second, the satellite images may not be suitable for other weather forecasting tasks such as rainfall prediction and solar radiation which are directly affected by the cloud situation, rather than the temperature forecasting. Second, Table 21 includes the results of temperature forecasting with input variables from other regions in the average MAEs with standard deviations.