应用数学和力学2013，Vol.34Issue(4)：408-419,12.DOI:10.3879/j.issn.1000-0887.2013.04.009
基于非等时距加权灰色模型与神经网络的组合预测算法
Combination Forecasting Algorithm Based on Non-Equal Interval Weighted Grey Model and Neural Network
摘要
Abstract
The non-equal interval forecasting algorithm plays an important role in trend analysis and forecasting of sequences with different intervals. Based on the traditional grey forecasting theory, a combination forecasting algorithm based on non-equal interval weighted grey model and neural network was proposed. By constructing the non-equal interval weighted grey forecasting model, the average of original data sequence was regarded as the initial value of cumulative sequence, the integral area of continuous accumulation function was used as the background value, and the cumulative sequence was processed by weighting in order to truly reflect the impact of time sequences development to forecasting results. On this basis, BP neural network was introduced to correct the residuals sequence of grey forecasting which further improved the forecasting accuracy. The numerical example indicates that the forecasting accuracy level of the algorithm is 1 and higher than similar algorithms.关键词
预测/非等时距/灰色模型/加权/神经网络/残差修正Key words
forecasting/ non-equal interval/ grey model/ weighted/ neural network/ residual modification分类
数理科学引用本文复制引用
韩晋,杨岳,陈峰,李雄兵..基于非等时距加权灰色模型与神经网络的组合预测算法[J].应用数学和力学,2013,34(4):408-419,12.基金项目
国家自然科学基金资助项目(51005252) （51005252）
