Method for predicting SFARIMA network traffic

Abstract

The invention provides a method for predicting network traffic and a prediction algorithm. The method, which compensates for the time-delay effect by continuously carrying out the time sliding on prediction sequences, comprises the following steps: Step 1, extracting a sample array from real traffic sequences, designating the sample array as FArray and initiating the values of three variables, M, N and m; Step 2, calculating the self-similarity index H of the sample array FArray on the basis of methods, such as periodogram, R/S analysis, wavelet analysis and the like; Step 3, estimating the order of the sample array by the AIC (Akaike Information Criterion), wherein, AIC(n,m) = lnsa + 2(n+m+1)/N (1), and determining that the order of the model is (p,q), if AIC(p,q) = min AIC(n,m); Step 4, calculating the model parameter ARMA [phi, theta], wherein, ARMA [phi, theta] = ARMA (pbest, qbest), and the calculating method comprises the following steps: (1) estimating the parameter of the autoregression part, and (2) estimating the average sliding coefficient; Step 5, calculating the coefficient vector, pij = theta1pij-1+ theta2pij-2+lambada+thetaqpij-q+phij(j>0), wherein, pi0 is equal to negative 1, and when j is larger than the sum of p and d, phij is equal to 0; and Step 6, predicting the network traffic according to the following formula: X(h) = *pij[(h)]X[t+h-j].

Description

technical field [0001] The invention is a prediction method of network flow, which is mainly used for solving the delay problem of flow prediction, and belongs to the field of computer application technology. Background technique [0002] The problem of traffic forecasting can be briefly summarized as: a set of traffic data X before the current moment is known n-i , (i=0, 1, 2Λ), then the flow rate X at a certain moment in the future n+k Available by X n-i inferred. Here k is called the step size, when k=1, it is a single-step prediction, and when k>1, it is a multi-step prediction. Studies have shown that 80% of network traffic can be predicted, and the accuracy of prediction is related to the selected time scale, and different sequences correspond to different optimal scales. This illustrates the high predictability of network traffic. [0003] The principle of the network traffic forecasting problem is that the sequences have a correlation structure or a fixed rel...

AI Technical Summary

Problems solved by technology

[0004] The main problems existing in the existing traffic forecasting methods are: large forecasti

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