Proceedings of the Korea Information Processing Society Conference (한국정보처리학회:학술대회논문집)
- 2018.10a
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- Pages.362-364
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- 2018
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- 2005-0011(pISSN)
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- 2671-7298(eISSN)
DOI QR Code
LSTM based Network Traffic Volume Prediction
LSTM 기반의 네트워크 트래픽 용량 예측
- Nguyen, Giang-Truong (Department of Electronics and Computer Engineering, Chonnam National University) ;
- Nguyen, Van-Quyet (Department of Electronics and Computer Engineering, Chonnam National University) ;
- Nguyen, Huu-Duy (Department of Electronics and Computer Engineering, Chonnam National University) ;
- Kim, Kyungbaek (Department of Electronics and Computer Engineering, Chonnam National University)
- Published : 2018.10.31
Abstract
Predicting network traffic volume has become a popular topic recently due to its support in many situations such as detecting abnormal network activities and provisioning network services. Especially, predicting the volume of the next upcoming traffic from the series of observed recent traffic volume is an interesting and challenging problem. In past, various techniques are researched by using time series forecasting methods such as moving averaging and exponential smoothing. In this paper, we propose a long short-term memory neural network (LSTM) based network traffic volume prediction method. The proposed method employs the changing rate of observed traffic volume, the corresponding time window index, and a seasonality factor indicating the changing trend as input features, and predicts the upcoming network traffic. The experiment results with real datasets proves that our proposed method works better than other time series forecasting methods in predicting upcoming network traffic.
Keywords