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].