Acknowledgement
"본 연구는 과학기술정보통신부 및 정보통신기획평가원의 대학ICT연구센터육성지원사업의 연구결과로 수행되었음" (IITP-2021-2016-0-00314)
References
- Bhuyan, Monowar H., Dhruba Kumar Bhattacharyya, and Jugal K. Kalita. "Network anomaly detection: methods, systems and tools." Ieee communications surveys & tutorials 16.1 (2013): 303-336. https://doi.org/10.1109/SURV.2013.052213.00046
- T. Alghamdi, K. Elgazzar, M. Bayoumi, T. Sharaf and S. Shah, "Forecasting Traffic Congestion Using ARIMA Modeling," 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), Tangier, Morocco, 2019, pp. 1227-1232, doi:10.1109/IWCMC.2019.8766698.
- R Vinayakumar, KP Soman, and Prabaharan Poornachandran. 2017. Applying deep learning approaches for network traffic prediction. In 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2353-2358.
- Zang, Chuanyun. "Deep Learning in Multiple Multistep Time Series Prediction." arXiv preprint arXiv:1710.04373 (2017).
- Haviluddin, Haviluddin, and Rayner Alfred. "Forecasting network activities using ARIMA method." (2014).
- Chan, Kit Yan, et al. "Traffic flow forecasting neural networks based on exponential smoothing method." 2011 6th IEEE Conference on Industrial Electronics and Applications. IEEE, 2011.
- Rutka, G. "Network traffic prediction using ARIMA and neural networks models." Elektronika ir Elektrotechnika 84.4 (2008): 53-58.
- Somenath Mukherjee, Rajdeep Ray, Rajkumar Samanta, Mofazzal H Khondekar, and Goutam Sanyal. 2017. Nonlinearity and chaos in wireless network traffic. Chaos, Solitons & Fractals 96 (2017), 23-29. https://doi.org/10.1016/j.chaos.2017.01.005
- Giang-Truong Nguyen, Van-Quyet Nguyen, Huu-Duy Nguyen, and Kyungbaek Kim. 2018. LSTM based Network Traffic Volume Prediction. In Proceedings of 2018 KIPS Spring Conference.
- Giang-Truong Nguyen, Van-Quyet Nguyen, Huu-Duy Nguyen, and Kyungbaek Kim. 2018. LSTM based Network Traffic Volume Prediction. In Proceedings of 2018 KIPS Spring Conference.
- H. Lu and F. Yang. 2018. Research on Network Traffic Prediction Based on Long Short-Term Memory Neural Network. In 2018 IEEE 4th International Conference on Computer and Communications (ICCC). 1109-1113.
- Cleveland, Robert B., et al. "STL: A seasonal-trend decomposition." J. Off. Stat 6.1 (1990): 3-73.
- Siami-Namini, Sima, Neda Tavakoli, and Akbar Siami Namin. "The performance of LSTM and BiLSTM in forecasting time series." 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019.
- Waleed Akbar, Afaq Muhammad, and Wang-Cheol Song, "Analysis of time-series user request pattern dataset for MEC-based video caching scenario", KNOM Review, Vol. 24, No. 1, Aug. 2021, pp. 20-28. https://doi.org/10.22670/KNOM.2021.24.1.20
- Min-A Kim and Seung-Joon Seok, "A Study of Virtual IoT System using Edge Computing", KNOM Review, Vol. 23, No. 1, Aug. 2020, pp. 51-62. https://doi.org/10.22670/KNOM.2020.23.1.51
- Doyung Lee, Seyon Jeon, Jonghwan Hyun, Jian Li, and James Won-Ki Hong, "Application-aware Traffic Engineering in SDN", KNOM Review, Vol. 19, No. 2, Dec. 2016, pp. 1-12. https://doi.org/10.22670/KNOM.2016.19.2.1
- Ntlangu, Mbulelo Brenwen, and Alireza Baghai-Wadji. "Modelling network traffic using time series analysis: A review." Proceedings of the International Conference on Big Data and Internet of Thing. 2017.
- Cheng, Changqing, et al. "Time series forecasting for nonlinear and non-stationary processes: a review and comparative study." Iie Transactions 47.10 (2015): 1053-1071. https://doi.org/10.1080/0740817X.2014.999180
- F. Pilka and M. Oravec, "Multi-step ahead prediction using neural networks," Proceedings ELMAR-2011, 2011, pp. 269-272.