Abstract
Although the grey forecasting model has been successfully adopted in various fields and demonstrated promising results, the literatures show its performance could be further improved. For this purpose, this paper proves that the growth rate of the simulated value of the grey model GM(1,1) is a fixed value. If the growth rates of the primary sequence are equate, the fitted value deriving from GM(1,1) is the same as the primary sequence, otherwise greater error would occur. In order to overcome shortcoming of the fixed growth rates, extend the traditional GM(1,1) model by introducing linear time-varying terms, which can predict more accurately on non geometric sequences, termed EGM(1,1). Finally, compared with the other improved grey model and ARIMA model, experimental results indicate that the proposed model obviously can improve the prediction accuracy.
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Acknowledgements
This work is supported in part by the National Natural Science Foundation (61001023, 61101004), the Aeronautical Science Fund (2010ZD53039) and the Natural Science Fund in Shaanxi Province (2010HQ8005) for the Northwestern Polytechnical University of China.
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Niu, W., Cheng, J. & Wang, G. Applications of extension grey prediction model for power system forecasting. J Comb Optim 26, 555–567 (2013). https://doi.org/10.1007/s10878-012-9477-8
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DOI: https://doi.org/10.1007/s10878-012-9477-8