河南科技大学学报：自然科学版

为了提高差分自回归移动平均模型的拟合精度,本文结合已有的文献,借助无约束优化方法来解决此模型中的参数估计问题。主要提出了一种改进的差分自回归移动平均模型参数的优化估计法,并对提出的算法进行详细说明,在强Wolfe条件下对全局收敛性进行了证明。该方法保证了迭代计算的收敛性,并且提高了收敛的速度。数值试验结果说明:该算法是一种较为有效的方法,与其他方法比较,参数估计值更为显著,提高了预测精度。

For improving the accuray of the autoregressive integrated moving average model,combined with the existing literature,the unconstrained optimization method was used to solve the problem of estimating the parameters in the model. An improved optimized algorithm was proposed,then the algorithm was descripted in detail and the global convergence was proved in the strong Wolfe conditions. Thus the algorithm not only ensures the convergence of iterative calculation,but also improvs the speed of convergence. Finally,the numerical experiments show that the algorithm is an effective method. Compared with other methods,the parameter estimation value is more remarkable and the prediction accuracy is improved.