Network traffic prediction method based on discrete wavelet transform and FA-ELM

Abstract

The invention provides a network traffic prediction method based on discrete wavelet transform and FA-ELM, the method comprising the following steps: step 1, constructing a DWAFE model, and setting a data administrator and a model administrator in the DWAFE model; and step 2, obtaining multiple pieces of network traffic data and sending the network traffic data to the data administrator, and carrying out data preprocessing on the network traffic data by the data administrator to obtain the network traffic data after data preprocessing. An FA-ELM model optimized by a firefly algorithm overcomes the defect of poor ELM stability, is high in accuracy of nonlinear data prediction, can be stably and reliably applied to research in various fields, and has great practical significance. The DWAFE model provided by the invention combines the respective advantages of an ARIMA model and the FA-ELM model, makes accurate network traffic prediction, and calculates a dynamic threshold interval under a specified confidence coefficient according to a prediction result, thereby realizing real-time sensing of an equipment operation state, and providing powerful support for equipment fault early warning.

Description

technical field [0001] The invention relates to the technical field of network traffic forecasting, in particular to a network traffic forecasting method based on discrete wavelet transform and FA-ELM. Background technique [0002] Autoregressive Integrated Moving Average model (ARIMA, Autoregressive Integrated Moving Average model), one of the most important and widely used models in time series forecasting, is suitable for dealing with time series with linear structure, but the forecast for nonlinear data is not satisfactory. In addition, insufficient generalization ability is one of its shortcomings, and its prediction accuracy will be significantly reduced as the prediction time prolongs, so it is only suitable for short-term prediction. [0003] Extreme Learning Machine (ELM, Extreme Learning Machine) has good fitting effect and high prediction accuracy for nonlinear data, but its robustness is not as good as ARIMA for linear data that is relatively easy to fit. Insuff...

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Problems solved by technology

[0005] The present invention provides a network traffic forecasting method b

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