2018년 추계 학술대회
Current Result Document :
| 한글제목(Korean Title) |
LSTM 기반의 네트워크 트래픽 용량 예측 |
| 영문제목(English Title) |
LSTM based Network Traffic Volume Prediction |
| 저자(Author) |
뉘엔양쯔엉
뉘엔반퀴엣
뉘엔휴쥐
김경백
Giang-Truong Nguyen
Van-Quyet Nguyen
Huu-Duy Nguyen
Kyungbaek Kim
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| 원문수록처(Citation) |
VOL 25 NO. 02 PP. 0362 ~ 0364 (2018. 11) |
한글내용
(Korean Abstract) |
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영문내용
(English 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.
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| 키워드(Keyword) |
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