# Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models
## 2 Descriptive Statistics
### Total Traffic Behavior  
Let us examine traffic behavior in this study. Table 5 provides further insight with descriptive statistics. For a specific user and application, the traffic can only flow in one direction. Therefore, there cannot be any upload traffic with non-zero download and vice versa, resulting in the minimum values of 0 in Table 5.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Application** & **No. Records** & **Application** & **No. Records** \\ \hline Web Applications & 9,641,283 & Others & 6,464,018 \\ Instant Messaging Applications & 5,540,289 & Games & 5,199,684 \\ File Transfer & 4,259,626 & Mail & 2,622,758 \\ Streaming Applications & 2,420,552 & VoIP & 1,825,777 \\ Security & 1,667,752 & Music Streaming & 790,107 \\ Network Operation & 635,634 & P2P Applications & 274,245 \\ Terminals & 121,807 & File Systems & 10,118 \\ DB Transactions & 5816 & Legacy Protocols & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Application classes.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Variable** & **Description** \\ \hline START\_HOUR & Start time of the one-hour period for measuring traffic \\ MASKED\_MSISDN & Masked mobile station international subscriber directory number \\ APP\_CLASS & Application class \\ UPLOAD & Incoming traffic in the uplink during one hour [MB] \\ DOWNLOAD & Outgoing traffic in the downlink during one hour [MB] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset variables.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**START\_HOUR** & **MASKED\_MSISDN** & **APP\_CLASS** & **UPLOAD** & **DOWNLOAD** \\ \hline
2018-02-10 01:00:00 & F6C1745A0A9 DF638 DE2C14683E0F250D & Streaming Applications & 0.002527 & 0.000616 \\
2018-02-10 01:00:00 & 6474B3E3E20B5887A7593C61439250A9 & Others & 0.000828 & 0.000334 \\
2018-02-10 01:00:00 & B05 DEBB3D0E2 ACD68 FE47611 CA3 FDDCB & Web Applications & 0.000967 & 0.001813 \\
2018-02-10 01:00:00 & B2D5516431 ECC5B6851 FD9 FBAE0387A7 & Games & 0.000039 & 0.000052 \\
2018-02-10 01:00:00 & 5B507 EECA75149121F9C86E8690109D8 & Mail & 0.012802 & 0.006137 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dataset records example.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Application** & **No. Records** & **Application** & **No. Records** \\ \hline Web Applications & 9,641,283 & Others & 6,464,018 \\ Instant Messaging Applications & 5,540,289 & Games & 5,199,684 \\ File Transfer & 4,259,626 & Mail & 2,622,758 \\ Streaming Applications & 2,420,552 & VoIP & 1,825,777 \\ Security & 1,667,752 & Music Streaming & 790,107 \\ Network Operation & 635,634 & P2P Applications & 274,245 \\ Terminals & 121,807 & File Systems & 10,118 \\ DB Transactions & 5816 & Legacy Protocols & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Application classes.  
Specifically, let us consider upload traffic. The standard deviation is 5.322763, and the mean is 0.5442475. These values suggest that the ratio of the standard deviation to the array values of the samples differs, indicating that the values are distributed over a wider range of data values. For download traffic, the situation is comparable: the standard deviation is 1.702829, and the mean is 0.05518938, with a significant difference between the array values.  
To assess the relationship between upload and download traffic, we used [PERSON]'s rank correlation coefficient, or [PERSON]'s \(\rho\)[18]. We chose this non-parametric method instead of the parametric [PERSON] method due to the fact that the Pearson criterion is applied to two quantitative indicators that have a linear relationship. [PERSON]'s method can be applied to any set of data without requiring additional preparation and processing of the values. Essentially, it allows for the determination of the strength of the relationship. For upload and download traffic with statistically significant differences at \(\rho<0.01\), a correlation coefficient of 0.914 indicates a very high correlation strength between upload and download traffic.