1 |
I. Aijaz and P. Agarwal, "A study on time series forecasting using hybridization of time series models and neural networks," Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science) Vol.13, No.5, pp.827-832, 2020.
|
2 |
I. G. Lee and M. H. Song, "A comparative study of statistical techniques and machine learning models for efficient leased line resource usage prediction," Proceedings of the KIPS, Vol.28., No.1, pp.474-476, 2021.
|
3 |
L. G. Roberts and B. D. Wessler, "Computer network development to achieve resource sharing," Proceedings of the May 5-7, 1970, Spring Joint Computer Conference, 1970.
|
4 |
H. M. Sigurdsson, S. E. Thorsteinsson, and T. K. Stidsen. "Cost optimization methods in the design of next generation networks," IEEE Communications Magazine, Vol.42, No.9, pp.118-122, 2004.
|
5 |
Statistical Office, "Business Basic Statistical Survey Report," Each Year (2020).
|
6 |
M. Joshi and T. H. Hadi, "A review of network traffic analysis and prediction techniques," arXiv preprint arXiv: 1507.05722, 2015.
|
7 |
S. J. Jung, D. J. Kim, Y. H. Know, and C. G. Kim, "A fitness verification of time series models for network traffic predictions," The Journal of Korea Information and Communications Society, Vol.29, No.2B, pp.217-227, 2004.
|
8 |
S. H. Ji, H. Hasanova, K. S. Shim, and M. S. Kim, "Prediction of traffic usage using machine learning algorithm for efficient network management," Proceedings of Symposium of the Korean Institute of communications and Information Sciences, pp.824-825, 2018.
|
9 |
W. Stallings, "SNMP and SNMPv2: the infrastructure for network management," IEEE Communications Magazine, Vol.36, No.3, pp.37-43, 1998.
DOI
|
10 |
K. McCloghrie and M. T. Rose, "RFC1213: Management information base for network management of TCP/IP-based internets: MIB-II," 1991.
|
11 |
M. T. Rose and K. McCloghrie, "RFC1155: Structure and identification of management information for TCP/IP-based internets," 1990.
|
12 |
R. G. Brown and R. F. Meyer, "The fundamental theorem of exponential smoothing," Operations Research, Vol.9, No.5, pp.673-685, 1961.
DOI
|
13 |
W. Yoo and A. Sim. "Time-series forecast modeling on high-bandwidth network measurements," Journal of Grid Computing, Vol.14, No.3, pp.463-476, 2016.
DOI
|
14 |
R. J. Hyndman and G. Athanasopoulos, "Forecasting: Principles and practice," OTexts, 2018. [Internet], Available from: https://otexts.com/fpp2
|
15 |
Colah's Blog, Understanding LSTM Networks [Internet], Available from: http://colah.github.io/posts/2015-08-Understanding-LSTMs/(2015)
|
16 |
R. Kumar, P. Kumar, and Y. Kumar, "Time series data prediction using iot and machine learning technique," Procedia Computer Science, Vol.167, pp.373-381, 2020.
DOI
|
17 |
A. Sherstinsky, "Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network," Physica D: Nonlinear Phenomena, Vol.404, pp.132306, 2020.
DOI
|
18 |
S. Hochreiter, and J. Schmidhuber, "Long short-term memory," Neural Computation, Vol.9, No.8, pp.1735-1780, 1997.
DOI
|
19 |
F. A. Gers, J. Schmidhuber, and F. Cummins, "Learning to forget: Continual prediction with LSTM," Neural Computation, Vol.12, No.10, pp.2451-2471, 2000.
DOI
|
20 |
B. Lim and S. Zohren, "Time-series forecasting with deep learning: A survey," Philosophical Transactions of the Royal Society A, Vol.379, No.2194, pp.20200209, 2021.
DOI
|
21 |
J. Zhao, et al., "Do rnn and lstm have long memory?," International Conference on Machine Learning, PMLR, 2020.
|
22 |
H. W. Taek, A. S. Jin, and C. J. Wook, "Forecasting technique of line utilization based on SNMP MIB-II using time series analysis," KIPS Journal, Vol.6, No.9, pp.2470-2478, 1999. DOI: 10.3745/KIPSTE.1999.6.9.2470.
DOI
|
23 |
J. D. Case, M. Fedor, M. L., Schoffstall, and J. Davin, "RFC1157: Simple network management protocol (snmp)," 1990.
|
24 |
G. E. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, "Time series analysis: Forecasting and control," John Wiley & Sons, 2015.
|