Correlation analysis is an effective mechanism for studying patterns in data and making predictions.Many interesting discoveries have been made by formulating correlations in seemingly unrelated data. We propose an al...Correlation analysis is an effective mechanism for studying patterns in data and making predictions.Many interesting discoveries have been made by formulating correlations in seemingly unrelated data. We propose an algorithm to quantify the theory of correlations and to give an intuitive, more accurate correlation coefficient.We propose a predictive metric to calculate correlations between paired values, known as the general rank-based correlation coefficient. It fulfills the five basic criteria of a predictive metric： independence from sample size,value between-1 and 1, measuring the degree of monotonicity, insensitivity to outliers, and intuitive demonstration.Furthermore, the metric has been validated by performing experiments using a real-time dataset and random number simulations. Mathematical derivations of the proposed equations have also been provided. We have compared it to Spearman＇s rank correlation coefficient. The comparison results show that the proposed metric fares better than the existing metric on all the predictive metric criteria.展开更多
Change trend of Chinese urban residents＇ per capita food-nitrogen annual consumption from 1981 to 2007 was analyzed and predicted by using ARIMA time-series model in order to reveal the change of urban food-nitrogen ...Change trend of Chinese urban residents＇ per capita food-nitrogen annual consumption from 1981 to 2007 was analyzed and predicted by using ARIMA time-series model in order to reveal the change of urban food-nitrogen consumption during the China＇s urbanization process.Results showed that after 1980s,the annual consumption of Chinese urban residents＇ food-nitrogen had a change trend of ＂ increase-decrease-increase＂ and generally presented as a slight increasing trend;With the acceleration of rapid economic development and urbanization process,Chinese urban residents＇ food-nitrogen consumption will still keep a rising trend in future,and also has a large rising space.展开更多
文摘Correlation analysis is an effective mechanism for studying patterns in data and making predictions.Many interesting discoveries have been made by formulating correlations in seemingly unrelated data. We propose an algorithm to quantify the theory of correlations and to give an intuitive, more accurate correlation coefficient.We propose a predictive metric to calculate correlations between paired values, known as the general rank-based correlation coefficient. It fulfills the five basic criteria of a predictive metric： independence from sample size,value between-1 and 1, measuring the degree of monotonicity, insensitivity to outliers, and intuitive demonstration.Furthermore, the metric has been validated by performing experiments using a real-time dataset and random number simulations. Mathematical derivations of the proposed equations have also been provided. We have compared it to Spearman＇s rank correlation coefficient. The comparison results show that the proposed metric fares better than the existing metric on all the predictive metric criteria.
基金Supported by State Council Special Fund for Pollution Sources Survey (WPXC2007C200)~~
文摘Change trend of Chinese urban residents＇ per capita food-nitrogen annual consumption from 1981 to 2007 was analyzed and predicted by using ARIMA time-series model in order to reveal the change of urban food-nitrogen consumption during the China＇s urbanization process.Results showed that after 1980s,the annual consumption of Chinese urban residents＇ food-nitrogen had a change trend of ＂ increase-decrease-increase＂ and generally presented as a slight increasing trend;With the acceleration of rapid economic development and urbanization process,Chinese urban residents＇ food-nitrogen consumption will still keep a rising trend in future,and also has a large rising space.
