ISSN 0253-2778

CN 34-1054/N

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Optimization model of correlation interval combination forecasting based on COWA operator and its approximate solutions

  • 1.

    School of Mathematical Sciences, Anhui University, Hefei 230601, China

  • 2.

    School of Economics, Anhui University, Hefei 230601, China

  • 3.

    School of Artificial Intelligence and Big Data, Hefei University,Hefei 230601,China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2020.05.015
  • Received Date: 10 June 2019
  • Accepted Date: 14 July 2019
  • Rev Recd Date: 14 July 2019
  • Publish Date: 31 May 2020
    Abstract

  • Abstract

    On the basis of Holt exponential smoothing model, multi-layer perceptron (MLP) model and support vector machine (SVM) model, an interval optimal combination forecasting model was built by introducing the concept of COWA operator and correlation coefficient and some properties of the model were discussed. At the same time, the correlation coefficient was used as the contribution index of the single point forecasting method in the combined forecasting, and the Shapley value in the cooperative game was combined to give the approximate solution of the corresponding optimal combined forecasting model. The data of WTI spot price were used to demonstrate the feasibility and effectiveness of the model, and the attitude parameters on COWA operator was done with the sensitivity analysis.

    Abstract

    On the basis of Holt exponential smoothing model, multi-layer perceptron (MLP) model and support vector machine (SVM) model, an interval optimal combination forecasting model was built by introducing the concept of COWA operator and correlation coefficient and some properties of the model were discussed. At the same time, the correlation coefficient was used as the contribution index of the single point forecasting method in the combined forecasting, and the Shapley value in the cooperative game was combined to give the approximate solution of the corresponding optimal combined forecasting model. The data of WTI spot price were used to demonstrate the feasibility and effectiveness of the model, and the attitude parameters on COWA operator was done with the sensitivity analysis.
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    • References(23)
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    尚文利,张立婷,李世超,等. 基于Holt指数平滑模型的油井产量动态预测[J].自动化与仪表, 2018, 33(4): 68-70.
    [3]
    常欣卓,杨开忠,李新,等.基于非线性自回归神经网络的局部大气密度预测方法[J].中国科学技术大学学报,2017,47(12):1015-1022.
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    张乐, 汪传旭. 基于GM(1,1)-MLP神经网络组合模型的物流总额预测[J]. 上海海事大学学报, 2018, 39(4):61-65.
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    ZAREI T, BEHYAD R. Predicting the water production of a solar seawater greenhouse desalination unit using multi-layer perceptron model[J]. Solar Energy, 2019, 177: 595-603.
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    赵春晓. 基于支持向量机的混沌时间序列预测方法的研究[D]. 沈阳：东北大学, 2008.
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    XU Y, YANG W, WANG J. Air quality early-warning system for cities in China[J]. Atmospheric Environment, 2017, 148:239-257.
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    唐小我,马永开,曾勇,杨桂元.现代组合预测和组合投资决策方法及应用研究[M]．北京：科学出版社，2003.
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    LAOUAFI A, MORDJAOUI M, HADDAD S, et al. Online electricity demand forecasting based on an effective forecast combination methodology[J]. Electric Power Systems Research, 2017, 148: 35-47.
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    XIONG T, LI C, BAO Y, et al. A combination method for interval forecasting of agricultural commodity futures prices[J]. Knowledge-Based Systems, 2015, 77: 92-102.
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    WANG J, HENG J, XIAO L, et al. Research and application of a combined model based on multi-objective optimization for multi-step ahead wind speed forecasting[J]. Energy, 2017, 125: 591-613.
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    沈家骅,严振祥.基于区间分析的组合预测系数确定方法[J].武汉理工大学学报(交通科学与工程版),2006(6):1077-1080.
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    SENGUPTA A, PAL T K. Fuzzy Preference Ordering of Interval Numbers in Decision Problems[M]. New York: Springer, 2009.
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    陈华友.组合预测方法有效性理论及其应用[M].北京:科学出版社,2008.
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    王建华.对策论[M].北京：清华大学出版社,1986.
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    陈华友,李翔,金磊,姚梦杰.基于相关系数及IOWA算子的区间组合预测方法[J].统计与决策, 2012 (6): 83-86.
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    朱家明,陈华友,周礼刚,等.基于ICOFWA算子的连续区间模糊组合预测模型及其应用[J].模糊系统与数学,2016,30(3):172-184.
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    陈华友,侯定丕.基于预测有效度的优性组合预测模型研究[J].中国科学技术大学学报,2002,32(2):172-180.)
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    [1]
    MAIA A L S, CARVALHO F D A T D. Holt’s exponential smoothing and neural network models for forecasting interval-valued time series[J]. International Journal of Forecasting, 2011, 27(3):740-759.
    [2]
    尚文利,张立婷,李世超,等. 基于Holt指数平滑模型的油井产量动态预测[J].自动化与仪表, 2018, 33(4): 68-70.
    [3]
    常欣卓,杨开忠,李新,等.基于非线性自回归神经网络的局部大气密度预测方法[J].中国科学技术大学学报,2017,47(12):1015-1022.
    [4]
    张乐, 汪传旭. 基于GM(1,1)-MLP神经网络组合模型的物流总额预测[J]. 上海海事大学学报, 2018, 39(4):61-65.
    [5]
    ZAREI T, BEHYAD R. Predicting the water production of a solar seawater greenhouse desalination unit using multi-layer perceptron model[J]. Solar Energy, 2019, 177: 595-603.
    [6]
    赵春晓. 基于支持向量机的混沌时间序列预测方法的研究[D]. 沈阳：东北大学, 2008.
    [7]
    XU Y, YANG W, WANG J. Air quality early-warning system for cities in China[J]. Atmospheric Environment, 2017, 148:239-257.
    [8]
    张莉,卢星凝,陆从林,等.支持向量机在高考成绩预测分析中的应用[J].中国科学技术大学学报,2017,47(1):1-9.
    [9]
    BATES J M, GRANGER C W J. The combination of forecast[J]. Operational Research Quarterly, 1969, 20(4): 451-468.
    [10]
    唐小我,马永开,曾勇,杨桂元.现代组合预测和组合投资决策方法及应用研究[M]．北京：科学出版社，2003.
    [12]
    LAOUAFI A, MORDJAOUI M, HADDAD S, et al. Online electricity demand forecasting based on an effective forecast combination methodology[J]. Electric Power Systems Research, 2017, 148: 35-47.
    [13]
    XIONG T, LI C, BAO Y, et al. A combination method for interval forecasting of agricultural commodity futures prices[J]. Knowledge-Based Systems, 2015, 77: 92-102.
    [14]
    WANG J, HENG J, XIAO L, et al. Research and application of a combined model based on multi-objective optimization for multi-step ahead wind speed forecasting[J]. Energy, 2017, 125: 591-613.
    [15]
    沈家骅,严振祥.基于区间分析的组合预测系数确定方法[J].武汉理工大学学报(交通科学与工程版),2006(6):1077-1080.
    [16]
    SENGUPTA A, PAL T K. Fuzzy Preference Ordering of Interval Numbers in Decision Problems[M]. New York: Springer, 2009.
    [17]
    YAGER R R. OWA aggregation over a continuous interval argument with applications to decision making[J]. IEEE Transactions on Systems, Man and Cybernetics: Part B,2004,34(5) :1952-1963..
    [18]
    MUKHERJEE S, GIROSI F, OSUNA E. Nonlinear prediction of chaotic time series using support vector machines[C]// Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop. IEEE, 1997: 511-520.
    [19]
    刘仁志,黄张裕,秦洁,等. 基于粒子群优化支持向量机的边坡稳定性预测[J]. 甘肃科学学报, 2019, 31(1):102-106.
    [20]
    陈华友.组合预测方法有效性理论及其应用[M].北京:科学出版社,2008.
    [21]
    王建华.对策论[M].北京：清华大学出版社,1986.
    [22]
    陈华友,李翔,金磊,姚梦杰.基于相关系数及IOWA算子的区间组合预测方法[J].统计与决策, 2012 (6): 83-86.
    [23]
    朱家明,陈华友,周礼刚,等.基于ICOFWA算子的连续区间模糊组合预测模型及其应用[J].模糊系统与数学,2016,30(3):172-184.
    [24]
    陈华友,侯定丕.基于预测有效度的优性组合预测模型研究[J].中国科学技术大学学报,2002,32(2):172-180.)
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