| As the rapid development and application of Internet, the scale of internet is becoming larger and larger, the features of network behavior are becoming complicated increasingly, which brings a great challenge to network planning,network management and quality of service. Predicting and modeling network traffic can bring out essential reference for bandwidth allocation, network traffic control, routing control, entry control and error control in network management.In the beginning, the article analyze the main character about network, then analyze and compare the advantage and disadvantage of some traditional network analytic model. After these analysis, a kind of grey predicting method based on error was given, which greatly improves the original residual error model via index processing on error sequence. And this new model was proved higher prediction accuracy by the experimental results. In the following, aiming at the shortages of combination Model based on Constant weight, a new model of combined forecasting method based on Fuzzy Adaptive variable weight was brought out, which uses fuzzy decision-making mechanism and the adaptive mechanism to gain the single model's fuzzy weights and the basic weight, Experimental results show that the model of combined forecasting method based on Fuzzy Adaptive variable weight is better than the combination Model based on Constant weight in the performance .But at the same time, when forecasting more than 7-step in the small time granularity network traffic prediction,, the prediction error will more than 20%, and have become a big trend. On the basis of the original model by adding dynamic mechanism, when the prediction error reaches a certain characteristic value, the variable weight combination model also will be reconstructed to adjust, thereby reducing the prediction error. Experimental results show that the dynamic variable weight combination forecasting model is better than the original model on the step size increased. |
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