East Asian J. Appl. Math., 12 (2022), pp. 264-284.
Published online: 2022-02
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The optimized grey multi-variable model, used to overcome the defects of the grey multi-variable model, is studied. Although this model represents a substantial improvement of the grey multi-variable one, unstable computation of the grey coefficients arising in ill-posed problems, may essentially diminish the model accuracy. Therefore, in the case of ill-posedness we employ regularization methods and use the generalized cross validation method to determine the regularization parameters. The methods developed are applied to the urban road short-term traffic flow prediction problem. Numerical simulations show that the methods proposed are highly accurate and outperform the grey multi-variate, the autoregressive integrated moving average, and the back propagation neural network models.
}, issn = {2079-7370}, doi = {https://doi.org/10.4208/eajam.280921.141121 }, url = {http://global-sci.org/intro/article_detail/eajam/20254.html} }The optimized grey multi-variable model, used to overcome the defects of the grey multi-variable model, is studied. Although this model represents a substantial improvement of the grey multi-variable one, unstable computation of the grey coefficients arising in ill-posed problems, may essentially diminish the model accuracy. Therefore, in the case of ill-posedness we employ regularization methods and use the generalized cross validation method to determine the regularization parameters. The methods developed are applied to the urban road short-term traffic flow prediction problem. Numerical simulations show that the methods proposed are highly accurate and outperform the grey multi-variate, the autoregressive integrated moving average, and the back propagation neural network models.