Zhou Jianguo
School of Economics and Management, North China Electric Power University, China
Zhang Xigang
School of Economics and Management, North China Electric Power University, China
ABSTRACT
The text is to establish a combined forecasting model based on the correlation coefficients theory, in consideration of the Chinese Carbon emissions from primary energy:uncertainty, imperfection and small sample properties. The Multiple regression prediction model not only consider the time factor but also takes into account the causal relationship between variables; the BP neural network is suitable for small samples, poor information system prediction;the gray system model can filter random amount mixed in raw data and find out the hidden rules in a volatile time series. We research combination forecasting method, from the point of view of the relevant indicators, used their advantage and theoretical correlation coefficient.This combined forecasting model is better than the traditional forecasting methods, It is able to improve the accuracy of the single prediction model. Finally, we predict the same period Chinese carbon emissions to verify the effectiveness of combination forecasting model, based on the data of the Chinese carbon emissions from 2002 to 2011 as well as the population, GDP and total energy consumption in the same period.
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How to cite this article
Zhou Jianguo and Zhang Xigang, 2013. Application of Correlation Coefficients Theory for the Prediction of Chinese
Carbon Emissions from Primary Energy. Information Technology Journal, 12: 4529-4533.
DOI: 10.3923/itj.2013.4529.4533
URL: https://scialert.net/abstract/?doi=itj.2013.4529.4533
DOI: 10.3923/itj.2013.4529.4533
URL: https://scialert.net/abstract/?doi=itj.2013.4529.4533