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Set Pair Analysis Based on Phase Space Reconstruction Model and Its Application in Forecasting Extreme Temperature

机译：基于相空间重构模型的集合对分析及其在极端温度预测中的应用

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摘要

In order to improve the precision of forecasting a time series, set pair analysis based on phase space reconstruction (SPA-PSR) model is established. In the new model, by using chaos analysis, we reconstruct the phase space with delay time and embedding dimension. Based on it, we rebuilt history sets and current sets in the SPA-PSR model. Two cases of forecasting extreme temperature in Mount Wutai and Datong are taken to examine the performance of SPA-PSR model. The results indicate that the mean relative error (MRE) of SPA-PSR model has decreased by 65.97%, 59.32%, and 7.79% in the case of Mount Wutai and 29.11%, 32.82%, and 9.03% in the case of Datong, respectively, compared with autoregression (AR) model, rank set pair analysis (R-SPA) model, and Back-Propagation (BP) neural network model. It gives a theoretical support for set pair analysis and improves precision of numerical forecasting.
机译：为了提高时间序列的预测精度，建立了基于相空间重构（SPA-PSR）模型的集合对分析方法。在新模型中，通过混沌分析，重构了具有延迟时间和嵌入维数的相空间。基于此，我们在SPA-PSR模型中重建了历史集和当前集。以五台山和大同的两个极端气温预报案例为例，研究了SPA-PSR模型的性能。结果表明，五台山地区SPA-PSR模型的平均相对误差（MRE）分别降低了65.97％，59.32％和7.79％，大同地区分别为29.11％，32.82％和9.03％，分别与自回归（AR）模型，秩集对分析（R-SPA）模型和反向传播（BP）神经网络模型进行比较。它为集合对分析提供了理论支持，并提高了数值预测的精度。

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  • 来源
    《Mathematical Problems in Engineering》 |2013年第9期|516150.1-516150.7|共7页
  • 作者

    Yin Zhang; Xiao-hua Yang; Ling Zhang; Wan-ying Ma; Ling-xia Qiao;

  • 作者单位

    School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China;

    State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China;

    Department of Mathematics and Statistics, Auburn University, Auburn, AL 36832, USA;

    School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China;

    School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China;

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