# Forecasting Daily Temperatures with Different Time Interval Data Using Deep Neural Networks
## 3 Proposed Neural Network Architectures  
We used three neural network structures, MLP, LSTM, and CNN, with two different types of time unit input data, daily and hourly, to forecast daily temperatures. We built the six models by pairing each structure with each time unit as follows:  
* MLP with daily input variables
* MLP with hourly input variables
* LSTM with daily input variables
* LSTM with hourly input variables
* CNN with daily input variables
* CNN with hourly input variables  
The detailed structures of each model are explained in Tables 1-6 and Figures 1 and 2. Note that \(T\) and \(m\) shown in Tables 1-6 denote the length of time of the input data and the number of input variables, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layers** & **Layer Type** & **Input Structure** & **Output Structure** \\ \hline
0–\(>\)1 & fully connected & \(T\times m\) & 2400 \\
1–\(>\)2 & fully connected & 2400 & 3600 \\
2–\(>\)3 & fully connected & 3600 & 2400 \\
3–\(>\)4 & fully connected & 2400 & 1600 \\
4–\(>\)5 & fully connected & 1600 & 1200 \\
5–\(>\)6 & fully connected & 1200 & 400 \\
6–\(>\)7 & fully connected & 400 & 100 \\
7–\(>\)8 & fully connected & 100 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The architecture of multilayer perceptron (MLP) with daily input data. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layers** & **Layer Type** & **Input Structure** & **Output Structure** \\ \hline
0–\(>\)1 & LSTM & (\(T\), \(m\)) & (\(T\), \(20\)) \\
1–\(>\)2 & LSTM & (\(T\), \(20\)) & (\(T\), \(20\)) \\
2–\(>\)3 & LSTM & (\(T\), \(20\)) & \(T\times 20\) \\
3–\(>\)4 & fully-connected & \(T\times 20\) & 1000 \\
4–\(>\)5 & fully-connected & 1000 & 400 \\
5–\(>\)6 & fully-connected & 400 & 100 \\
6–\(>\)7 & fully-connected & 100 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The architecture of LSTM with hourly input data. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layers** & **Layer Type** & **Input Structure** & **Output Structure** \\ \hline
0–\(>\)1 & fully connected & \(T\times m\) & 2400 \\
1–\(>\)2 & fully connected & 2400 & 3600 \\
2–\(>\)3 & fully connected & 3600 & 2400 \\
3–\(>\)4 & fully connected & 2400 & 1600 \\
4–\(>\)5 & fully connected & 1600 & 1200 \\
5–\(>\)6 & fully connected & 1200 & 400 \\
6–\(>\)7 & fully connected & 400 & 100 \\
7–\(>\)8 & fully connected & 100 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The architecture of MLP with hourly input data. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layers** & **Layer Type** & **Input Structure** & **Output Structure** \\ \hline
0–\(>\)1 & LSTM & (\(T\), \(m\)) & (\(T\), \(20\)) \\
1–\(>\)2 & LSTM & (\(T\), \(20\)) & (\(T\), \(20\)) \\
2–\(>\)3 & LSTM & (\(T\), \(20\)) & \(T\times 20\) \\
3–\(>\)4 & fully-connected & \(T\times 20\) & 1000 \\
4–\(>\)5 & fully-connected & 1000 & 400 \\
5–\(>\)6 & fully-connected & 400 & 100 \\
6–\(>\)7 & fully-connected & 100 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The architecture of LSTM with daily input data. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layers** & **Layer Type** & **Output Structure** & **Filter Size** & **Number of Filter** & **Stride** \\ \hline
0-\(>\)1 & convolution & (T/2, _m_) & (2, 1) & 8 & (2,1) \\
1-\(>\)2 & convolution & (T/4, _m_) & (2, 1) & 8 & (2,1) \\
2-\(>\)3 & convolution & (T/8, _m_) & (2, 1) & 10 & (2,1) \\
3-\(>\)4 & convolution & (7/16, _m_) & (2, 1) & 12 & (2,1) \\
4-\(>\)5 & convolution & (1, _m_) & (7/16,1) & 24 & 1 \\
5-\(>\)6 & convolution & 450 & (1, _m_) & 450 & - \\
6-\(>\)7 & fully connected & 120 & - & - & - \\
7-\(>\)8 & fully connected & 70 & - & - & - \\
8-\(>\)9 & fully connected & 1 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The architecture of CNN with hourly input data. Figure 1: Model structure of long-short term memory (LSTM) for temperature forecasting.