CAJ | 학술논문

提出一种基于近似熵测度的变权组合预测方法.首先,不同于传统的预测效果评价准则,从衡量样本序列复杂性的角度出发,以预测值误差序列的近似熵测度为评价效果准则,建立变权组合预测优化模型;然后,在变权组合预测权值分配问题上,为克服常规的均值估计法和回归分析法的不足,采用在线最小二乘支持向量机(LS-SVM)回归法,实现预测点加权系数的准确预测;最后,通过实例表明了该方法的可行性和有效性.
제출일충기우근사적측도적변권조합예측방법.수선,불동우전통적예측효과평개준칙,종형량양본서렬복잡성적각도출발,이예측치오차서렬적근사적측도위평개효과준칙,건립변권조합예측우화모형;연후,재변권조합예측권치분배문제상,위극복상규적균치고계법화회귀분석법적불족,채용재선최소이승지지향량궤(LS-SVM)회귀법,실현예측점가권계수적준학예측;최후,통과실례표명료해방법적가행성화유효성.
A variable weight combined forecasting method based on approximate entropy is proposed. Firstly, unlike the traditional evaluation criterion, and considering measurement complexity of sequential sample, an optimizing model of variable weight combined forecasting is established according to the approximate entropy of the prediction error sequential. Then, the weight allocation problem is considered. To avoid the insufficiency of the conventional method (e.g. mean estimation and regression analysis), online least squares support vector machine(LS-SVM) regression method is used to achieve accurate forecasting about weight. Finally, an example shows the feasibility and effectiveness of the proposed method.