Educational Bandwidth Traffic Prediction using Non-Linear Autoregressive Neural Networks
Conference paper
Oumar, O A, Dyllon, S, Xiao, P and Hong, T (2018). Educational Bandwidth Traffic Prediction using Non-Linear Autoregressive Neural Networks. The 21st International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines - CLAWAR 2018. Panama 10 - 12 Sep 2018
| Authors | Oumar, O A, Dyllon, S, Xiao, P and Hong, T |
|---|---|
| Type | Conference paper |
| Abstract | Time series network traffic analysis and forecasting are important for fundamental to many decision-making processes, also to understand network performance, reliability and security, as well as to identify potential problems. This paper provides the latest work at London South Bank University (LSBU) network data traffic analysis by adapting nonlinear autoregressive exogenous model (NARX) based on the Levenberg-Marquardt backpropagation algorithm. This technique can analyze and predict data usage in its current and future states, as well as visualise the hourly, daily, weekly, monthly, and quarterly activities with less computation requirement. Results and analysis proved the accuracy of the prediction techniques. |
| Year | 2018 |
| Accepted author manuscript | License File Access Level Open |
| Publication dates | |
| 10 Sep 2018 | |
| Publication process dates | |
| Deposited | 29 May 2019 |
| Accepted | 10 May 2018 |
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