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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 on London South Bank University (LSBU) network data traffic analysis by adapting nonlinear autoregressive exogenous model (NARX) based on Levenberg-Marquardt backpropagation algorithm. This technique can analyse 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.

In the foundry, the surface dry furnace is a special equipment for surface drying after sand core hydrophobic coating. In order to accurately predict whether it was possible to malfunction, four objective variables was used as input, and the health status of the equipment was used as the output. A prediction model based on the traditional BP neural network was established. This model combined genetic algorithm (GA) to optimize the initial weight of BP neural network; combined with LM (Levenberg-Marquardt) algorithm to improve the BP neural network, the error decreased too slowly when the predicted value approached the target value. Four kinds of evaluation methods were used in Matlab to compare the prediction results of the three models in simulation training. The research shows that the improved algorithm can overcome the problem that the traditional BP neural network has slow convergence rate and is easy to fall into the local optimal solution, and it has higher prediction accuracy, which provides a new solution to the fault prediction of the surface dry furnace.