The invention discloses a method for predicting
elastic cloud computing resources based on an SARIMA-WNN model. The method comprises the steps that complementary advantages are achieved by using a seasonal
autoregressive integrated moving average (SARIMA) model combining with a
wavelet neural network (WNN) prediction model to improve the prediction accuracy; according to the SARIMA, seasonal periodic factors are added on the basis of an ARIMA model, periodic cloud resource demand data of a past section is input to a SARIMA (q, d, q)(P, D, Q) s model to obtain d, p, q, D, P and Q respectively;prediction is conducted on tranquilized and sequenced codes by using the SARIMA model, and a prediction result is marked as and an L
residual value is marked as rt, wherein the prediction result andthe
residual value can be obtained through the prediction; a model which conforms to
elastic cloud source prediction is obtained through conducting training on the WNN network by using training samples, prediction is conducted aiming at the residual sequence rt, and the prediction result is marked as ; finally the prediction result of the SARIMA-WNN combined model is obtained. By means of the method, the problems of inaccuracy of a
single model, poor effects of other combined models and the like are solved.