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The combined forecasting method of GM(1,1) with linear regression and its application

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2 Author(s)
Li Bing-jun ; Henan Agric. Univ., Zhengzhou ; He Chun-hua

Linear regression analysis could get better results in a short-term forecast. However, when some aberrant points exist in a given raw data sequence, it will be difficult for the linear regression function to accurately predict the changing tendency of the data sequence. To solve the problem, firstly, the raw data sequence with some abnormal data is classified into two parts: aberrant data and normal data; then, applying the principle of grey disaster, we can make use of GM (1,1) to forecast the possible aberrant date points in the future based on the aberrant data, and for other normal data points, linear regression function can be applied to get a forecast value. By applying the combined method to the prediction of the gross domestic production of Henan province, it showed that the new method could achieve better forecasting results compared with other forecasting models, and make up for some deficiencies in GM (1,1) model and linear regression model in a sense.

Published in:

Grey Systems and Intelligent Services, 2007. GSIS 2007. IEEE International Conference on

Date of Conference:

18-20 Nov. 2007