区间离散二阶差分方程——BP神经网络组合预测方法
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摘要
针对小样本且具有较强波动性的区间时间序列的预测问题,文章提出了一种区间离散二阶差分方程——BP神经网络组合预测新方法,并讨论模型的相关性质,该模型对拐点区间数据具有较好的预测能力。实证预测结果表明,所提出的预测方法不但适用于小样本区间时间序列预测,对区间序列波动细节有较强的预测能力,而且比现有的区间时间序列预测模型有更高的预测精度。
For the prediction problem of interval time series with small samples and strong volatility, this paper proposes a new combined forecasting method of interval discrete second-order difference equation(Ⅰ-DDE) and BP neural network,and also discusses the related properties of the model. This model has good prediction ability for inflexion interval data. The empirical prediction results show that the proposed prediction method is not only applicable to the prediction of interval time series with small samples, but also has a strong ability to predict the fluctuation details of interval series, and has a higher prediction accuracy than other existing interval time series prediction models.
For the prediction problem of interval time series with small samples and strong volatility, this paper proposes a new combined forecasting method of interval discrete second-order difference equation(Ⅰ-DDE) and BP neural network,and also discusses the related properties of the model. This model has good prediction ability for inflexion interval data. The empirical prediction results show that the proposed prediction method is not only applicable to the prediction of interval time series with small samples, but also has a strong ability to predict the fluctuation details of interval series, and has a higher prediction accuracy than other existing interval time series prediction models.
引文
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[4]李全中.我国财政收入的灰色区间预测及精度检验[J].统计与决策, 2012,(12).
[5]孟伟,刘思峰,曾波.区间灰数的标准化及其预测模型的构建与应用研究[J].控制与决策, 2012,(5).
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[9]刘金培,林盛,郭涛,陈华友.一种非线性时间序列预测模型及对原油价格的预测[J].管理科学, 2011, 24(6).
[10]Xiong T. A Combination Method for Interval Forecasting of Agricultural Commodity Futures Prices[J]. Knowledge-Based Systems, 2015,(77).
[11]杨威,韩艾,汪寿阳.基于区间型数据的金融时间序列预测研究[J].系统工程学报, 2016, 31(6).
[12]朱家明,陈华友,周礼刚,刘金培.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).
[13]万昆,柳瑞禹.区间时间序列向量自回归模型在短期电力负荷预测中的应用[J].电网技术,2012, 11(36).
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