Combination Forecasting Model for Predicting the Shelf Life of Two-StateMaterials Based on Support Vector Machine

摘要

A combination forecasting model based on Support Vector Machine (SVM) whose objective is to minimize thestructure risk is proposed. The storage failure of two-state materials tends to fail immediately without any recognizabledefeats prior to the failure, which increases the difficulty of forecasting, so the combination forecasting model is oftenused to optimize the prediction effect. The core ideas of previous combination forecasting models such as those based onforecasting error and those based on nonlinear weighted average are finding the optimal weights, but the structure offorecasting model is fixed. In this paper, three single forecasting models, Weibull distribution statistic method, BP neuralnetwork prediction method and SPFM (Sliding Polynomial Fitting Method) are chosen in which their forecastmechanisms are completely different. The results of single forecasting methods are used as training set of SVM. By usinglibsvm toolbox, we can get the nonlinear mapping functions that have the minimum structure risk. At last, a simulation isconducted to verify this model by using the data from Petroleum Center.