Combination Forecasting Algorithm Based on Non-Equal Interval Weighted Grey Model and Neural Network
-
摘要: 非等时距预测算法在不等时间间隔序列的趋势分析与预测方面具有重要作用.在传统灰色预测理论的基础上,提出一种基于非等时距加权灰色模型和神经网络的组合预测算法.通过构建非等时距加权灰色预测模型,将原始数据序列的平均值作为累加序列初值,将连续累积函数的积分面积作为背景值,对累加序列进行加权处理,以真实反映时间序列发展对预测结果的影响.在此基础上,引入BP神经网络对灰色预测的残差序列进行修正,进一步提高了预测精度.经算例验证,该算法预测精度达到1级,且高于类似算法.Abstract: The nonequal 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 nonequal interval weighted grey model and neural network was proposed. By constructing the nonequal 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.
-
Key words:
- forecasting /
- non-equal interval /
- grey model /
- weighted /
- neural network /
- residual modification
-
[1] Lin Y H, Lee P C, Chang T P. Adaptive and highprecision grey forecasting model[J].Expert System Application, 2009,36(2): 9658-9662. [2] Alippi C, Piuri V. Experimental neural networks for prediction and identification[J].IEEE Trans Instrum Meas,1996, 45(4): 670-676. [3] 邓聚龙. 灰色系统理论教程[M]. 武汉:华中科技大学出版社, 1990.(DEN Ju-long.Grey System Theory Tutorial [M]. Wuhan: Huazhong University of Science and Technology Press, 1990.(in Chinese)) [4] 宋中明, 肖新平.反向累加生成及灰色GOM(1, 1)模型[J].武汉理工大学学报, 2002, 26(4): 531553.(SONG Zhong-ming, XIAO Xin-ping. The accumulated generating operation in opposite direction and its use in grey model GOM(1, 1)[J].Journal of Wuhan University of Technology, 2002, 26(4): 531-553.(in Chinese)) [5] 杨知, 任鹏, 党耀国.反向累加生成及灰色GOM(1, 1)模型优化[J]. 系统工程理论与实践, 2009, 29(8): 160-164.(YANG Zhi,REN Peng,DANG Yao-guo. The accumulated generating operation in opposite direction and GOM(1, 1) model optimization[J].Systems Engineering Theory & Practice,2009, 29(8): 160-164.(in Chinese)) [6] 李玮. 非等间距GM(1, 1)组合预测模型[J]. 陕西理工学院学报(自然科学版), 2007, 23(3):71-74.(LI Wei. Non-equigap combination gray forecast model[J].Journal of Shaanxi University of Technology(Natural Science Edition ),2007, 23(3):71-74. (in Chinese)) [7] WANG Chao-huang, HU Li-chang. Using genetic algorithms grey theory to forecast high technology industrial output[J].Applied Mathematics and Computation, 2008, 195(6):256-263. [8] 王春超,王丽萍,曹云慧,朱艳霞,张验科. 改进多变量灰色模型在城市用水量预测中的应用[J].水电能源科学, 2013, 31(2):27-29.(WANG Chun-chao, WANG Li-ping, CAO Yun-hui, ZHU Yan-xia, ZHANG Yan-ke. Application of improved multivariable grey model in prediction of urban water consumption[J].Water Resources and Power, 2013, 31(2):27-29. (in Chinese)) [9] 焦淑华, 夏冰, 徐海静. BP神经网络预测的MATLAB实现[J].哈尔滨金融高等专科学校学报, 2009, 97(6): 55-56.(JIAO Shu-hua, XIA Bin, XU Hai-jing. BP neural network in MATLAB[J].Journal of Harbin Senior Finance College, 2009, 97(6): 55-56. (in Chinese)) [10] 陈夫进, 王宝成. 基于BP神经网络系统的短期电力负荷预测[J]. 河南科学, 2013, 31(2):168-171.( CHEN Fu-jin, WANG Bao-cheng. Short-term power load forecasting based on BP neural network system[J]. Henan Science, 2013, 31(2):168-171. (in Chinese)) [11] 叶世伟.神经网络原理[M].北京:机械工业出版社, 2004.( YE Shi-wei.Principles of Neural Networks [M].Beijing: China Machine Press, 2004. (in Chinese)) [12] 史峰, 王小川, 郁磊, 李洋. MATLAB神经网络30个案例分析[M].北京:北京航空航天大学出版社, 2010: 9-10.(SHI Feng, WANG Xiao-chuan, YU Lei, LI Yang.30 Cases Study of Neural Network Based on MATLAB [M].Beijing: Beijing University of Aeronautics and Astronautics Press, 2010:9-10. (in Chinese)) [13] 杨春波. 基于灰色模型与人工神经网络的改进组合预测模型及其应用研究[D].山东:山东师范大学, 2009.(YANG Chun-bo. Improved combination forecasting model and its application based on grey model and artificial neural network [D].Shandong: Shandong Normal University, 2009. (in Chinese)) [14] 杨星, 朱大栋, 何勇, 王蔚. 基于灰色神经网络模型下的船闸货运量预测[J]. 武汉理工大学学报(交通科学与工程版), 2013, 37(1):120-123.(YANG Xing, ZHU Da-dong, HE Yong, WANG Wei. Forecast of the lock freight volume based on grey neural network algorithm[J]. Journal of Wuhan University of Technology(Transportation Science & Engineering), 2013, 37(1):120-123. (in Chinese)) [15] Granger C W J, Ramanathan R. Improved methods of combining forecasts [J].Journal of Forecasting, 1984,3(2):197-204. [16] Kin K L, Yu L, Wang S Y, Huang W. Hubridizing exponential smoothing and neural network for financial time series predication[J].Lecture Notes in Computer Science,2006, 3994:493-500. [17] Bunn D W. Forecasting with more than one model [J].Journal of Forecasting, 1989, 8(3):161-166. [18] Luo D, Liu S F, Dang Y G. The optimization of grey model GM(1, 1)[J].J Engineering Science,2003, 5(8):50-53. [19] 杨喜中, 白莉. 非等时距加权灰色预测模型及其在形变预报中的应用[J]. 城市勘测, 1996(1):2022.(YANG Xi-zhong, BAI Li. Non-equal interval weighted grey forecast model and its application in deformation forecast[J].Urban Geotechnical Investigation & Surveying,1996(1):20-22. (in Chinese)) [20] 刘辉, 田红旗, 李燕飞. 基于小波分析法与神经网络法的非平稳风速信号短期预测优化算法[J].中南大学学报(自然科学版), 2011, 42(9):2704-2711.(LIU Hui, TIAN Hong-qi, LI Yan-fei. Short-term forecasting optimization algorithm for unsteady wind speed signal based on wavelet analysis method and neutral networks method[J].Journal of Central South University(Science and Technology), 2011, 42(9):2704-2711. (in Chinese)) [21] 曲建军, 高亮, 田新宇, 辛涛. 基于灰色理论的轨道几何状态中长期时变参数预测模型的研究[J].铁道学报, 2010, 32(2): 55-59.(QU Jian-jun, GAO Liang, TIAN Xin-yu, XIN Tao. Study on the mid & long term prediction model of track geometry state based on the grey timevarying parameters theory[J].Journal of the China Railway Society, 2010, 32(2): 55-59. (in Chinese)) [22] 何庆飞, 陈桂明, 陈小虎, 姚春江. 基于改进灰色神经网络的液压泵寿命预测[J].中国机械工程, 2013, 24(4):500-506.(HE Qing-fei, CHEN Gui-ming, CHEN Xiao-hu, YAO Chun-jiang. Life prediction of hydraulic pump based on an improved grey neural network[J].China Mechanical Engineering, 2013, 24(4):500-506. (in Chinese)) [23] 曾祥艳, 曾玲. 非等间距GM(1, 1)模型的改进与应用[J].数学的实践与认知, 2011, 41(2):9095.(ZENG Xiang-yan, ZENG Ling. Improvement of non-equidistant GM(1, 1) model and its application[J].Mathematics in Practice and Theory,2011, 41(2):90-95. (in Chinese)) [24] 雍华, 魏勇, 孔新海. 同时优化背景值和灰导数的新非等间距GM(1, 1)模型[J].数学的实践与认知, 2011, 41(20):172-178.(YONG Hua, WEI Yong, KONG Xin-hai. One new non-equidistance GM(1, 1) optimized the grey derivative and the background value at the same time[J].Mathematics in Practice and Theory, 2011, 41(20):172-178. (in Chinese)) [25] 戴文战, 李俊峰.非等间距GM(1, 1)模型建模研究[J].系统工程理论与实践, 2005, 25(9):89-93.(DAI Wen-zhan, LI Jun-feng. Modeling research on nonequidistance GM(1, 1) model[J].Systems EngineeringTheory & Practice, 2005, 25(9):89-93. (in Chinese))
点击查看大图
计量
- 文章访问数: 1602
- HTML全文浏览量: 64
- PDF下载量: 1628
- 被引次数: 0