A Novel Discrete Multivariate Grey Forecasting Model with L2 Regularization Term
The MGM(1,m,N)model has three problems:Non-homologous param-eters,simple model structure,and multicollinearity between variables.In order to solve this defects of MGM(1,m,N)model,the new structure MGM(1,m,N)is built,which modifies the model structure by introducing the linear correction term and the grey action term into the original model.In order to solve the defects in the pa-rameter application,using the derivative first-order difference formula and recursive method to solve the time response function of NSMGM(1,m,N)model.To address the adverse effects of multicollinearity,the parameter estimation method is improved from reducing the variance of parameter estimators.The L2 regularization term is introduced into the ordinary least square estimation and the optimal L2 regular term parameter is solved by the particle swarm algorithm.Finally,the novel model is applied to the forecast of China's three major staple grain yields.The results show that the novel model solves the problems in the parameter application and model structure of MGM(1,m,N)model in certain degree.The optimized model can effec-tively alleviate the influence of model's predictive performance by multicollinearity and improves the MGM(1,m,N)the model's predictive precision.
The MGM(1,m,N)modelmulticollinearityL2 regular termparticle swarm algorithm
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