基于“序列相对贴近度”的组合预测权值分配  

Weight allocation of combination prediction based on sequence relative nearness degree

作　　者：吕永乐[1] 郎荣玲[1] 谈展中[1] 

机构地区：[1]北京航空航天大学电子信息工程学院,北京100191

出　　处：《北京航空航天大学学报》2009年第12期1434-1437,共4页Journal of Beijing University of Aeronautics and Astronautics

基　　金：国防十一五预研项目资助(102010201)

摘　　要：在时间序列的组合预测权值分配问题上,为克服传统的均方误差倒数加权、熵权和最优化方法之不足,从预测值序列与评价样本序列间的贴近性出发,提出新方法综合衡量单一参与模型的适用性,并据此分配权值.详细给出了序列相对贴近度(SRND,Sequence Relative Nearness Degree)及与之相关的"序列趋势关联度"和"尺度区间熵"的概念,并提出基于SRND的权值分配方法.将SRND权值分配方法应用于航空发动机排气温度裕度参数时间序列的联合自回归滑动平均模型、函数系数自回归模型和径向基函数网络预测,有效地提高了预测准确度,获得优于均方误差倒数加权和熵权方法的组合性能,且运算量远小于最优化方法.Aiming at the weight allocation problems of combination prediction for a time series, a new method was proposed to evaluate the applicability of the employed models and allocate weights, based on the ＂nearness＂ between the test sequence and the corresponding prediction value sequence, which overcame the shortages of existing methods such as mean square error reciprocal weight （1/MSE） , entropy weight and optimization weight. The definitions of sequence relative nearness degree （SRND） , related sequence trend association and scale interval entropy were given and well discussed, as well as the weight allocation expressions based on SRND. By the example which combined the autoregressive moving average model, functional-coefficient autoregressive model and radial basis function prediction networks in the prediction analysis for the takeoff exhaust gas temperature margin time series, the conclusion is drawn that the prediction accuracy can be effectively improved with the proposed method, compared to 1/MSE and entropy weight methods, while the calculation mount is far lower than optimization weight method.

关 键 词：时间序列分析 组合预测 建模 性能 

分 类 号：O23[理学—运筹学与控制论] TP18[理学—数学]