Abstract
It is crucial for network operators to predict network traffic in the future as accurate as possible for appropriate resource provisioning and traffic engineering. Recurrent neural network (RNN) methods are considered to be the most promising prediction methods because of their high prediction accuracy. In conventional studies, RNN methods use only time series of traffic volume as input, and do not use any attribute information (e.g., timestamp and day of the week) of the time series data. However, traffic volume changes depending on both time and day of the week. Therefore, it is possible that we can further improve the prediction accuracy of the RNN methods by using the attribute information as input, in addition to the time series of traffic volume. In this paper, we investigate the effect of using the attribute information of time series of traffic volume on prediction accuracy in network traffic prediction. We propose two RNN methods: RNN-VT method and RNN-VTD method. The RNN-VT method uses timestamp information and the RNN-VTD method uses both timestamp and day of the week information as input, in addition to the time series of traffic volume. Experimental results show that day of the week information is significantly effective for improving prediction accuracy of the RNN methods while timestamp information is not effective.
Original language | English |
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Title of host publication | 9th International Conference on Information and Communication Technology Convergence |
Subtitle of host publication | ICT Convergence Powered by Smart Intelligence, ICTC 2018 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 521-525 |
Number of pages | 5 |
ISBN (Electronic) | 9781538650400 |
DOIs | |
Publication status | Published - Nov 16 2018 |
Event | 9th International Conference on Information and Communication Technology Convergence, ICTC 2018 - Jeju Island, Korea, Republic of Duration: Oct 17 2018 → Oct 19 2018 |
Other
Other | 9th International Conference on Information and Communication Technology Convergence, ICTC 2018 |
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Country/Territory | Korea, Republic of |
City | Jeju Island |
Period | 10/17/18 → 10/19/18 |
Keywords
- Deep Learning
- Network Traffic Prediction
- Recurrent Neural Network
- The Internet traffic
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
- Information Systems
- Information Systems and Management
- Artificial Intelligence