数据挖掘论文英文版.doc

Jilin Provinces population growth and energy consumption analysisDemonstrationIllustrationConduction & suggestionEnglishMajor Statistics Student No. 0401083710 Name Niu Fukuan Jilin Provinces population growth andenergy consumption analysisSummarySince the third technological revolution, the energy has become the lifeline of national economy, while the energy on Earth is limited, so in between the major powers led to a number of oil-related or simply a war for oil. In order to compete on the worlds resources and energy control, led to the outbreak of two world wars. Chinas current consumption period coincided with the advent of high-energy, CNPC, Sinopec, CNOOC three state-owned oil giants have been going out to develop international markets, Jilin Province as Chinas energy output and energy consumption province, is also active in the energy corresponding diplomacy. Economic globalization and increasingly fierce competition in the energy environment, Chinas energy policy is still there are many imperfections, to a certain extent, affect the energy and population development of Jilin Province, China and even to some extent can be said existing population crisis is the energy crisis.KeywordEnergy consumption; Population; Growth; Analysis;Data sourceI select data from China Statistical Yearbook 2009 Jilin Province 1995-2007 comprehensive annual financial data (Table 1). Record of the total population (end) of the annual data sequence Xt, mind full of energy consumption (kg of standard coal) annual data sequence Yt.Table 1 1995-2007 older and province GDP per capita consumption level of all dataYearsOf the total population XtNational energy consumption (kg) YtLN Of the total population XtLN National energy consumption(kg) Yt199512112115842626.811.7045453216.57821476199612238917807599.511.7149597816.69513586199712362616454620.611.7250161616.61611688199812476114459799.911.7341551916.48688294199912578615270420.411.7423373316.54142821200012674316020315.211.7499166916.58936817200112762716629798.111.7568672316.62670671200212845317585215.711.7633183616.68256909200312922719888035.311.7693258316.80562887200412998821344029.611.7751974216.87628261200513075623523004.411.7810882716.97348941200613144825592925.611.7863666217.05782653200713212926861825.711.79153417.106216721. Timing diagramFirst, the total population of Table 1 (end) of the annual data series Xt, full of energy consumption (kg of standard coal) annual data series Yt are drawn timing diagram, in order to observe the annual population data series Xt and national annual energy consumption data sequence Yt is stationary, by EVIEWS software output is shown below.Figure 1 of the total population (end) sequence timing diagramFigure 2 universal life energy consumption (kg of standard coal) sequence timing diagramFigure 1 is a sequence Xt the timing diagram, Figure 2 is a sequence Yt of the timing diagram.Two figures show both the total population (end) or universal life energy consumption (kg of standard coal) index showed a rising trend, the total population of the annual data series Xt and national annual energy consumption data sequence Yt not smooth, the two may have long-term cointegration relationship.2. Data smoothing(1)Sequence Logarithm Figures 1 and 2 by the intuitive discovery data sequence Xt and Yt showed a significant growth trend, a significant non-stationary sequence. Therefore, the total population of first sequence Xt and universal life energy consumption (kg of standard coal) Yt, respectively for the number of treatment to eliminate heteroscedasticity. That logx = lnXt, logy = lnYt, with a view to the target sequence into the linear trend trend sequence, by EVIEWS software operations, the number of sequence timing diagram, in which the population sequence logx timing diagram shown in Figure 3, the full sequence of energy consumption logy timing diagram shown in Figure 4.Figure3 Figure 4Figure 3 shows the total population observed sequence logx and universal life energy consumption (kg of standard coal) sequence logy index trend has been basically eliminated, the two have obvious long-term cointegration relationship, which is the transfer function modeling an important prerequisite. However, the above sequence of numbers is still non-stationary series. Respectively logx and logy sequence of ADF unit root test (Table 5 and Table 6), the test results as shown below.(2)Unit root testHere we will be on the provinces total population and the whole sequence Xt energy consumption (kg of standard coal) sequence data Yt be the unit root test, the results obtained by Eviews software operation is as follows:Table 2 Of the total population sequence logxObtained from Table 2: Total population sequence data Xt of the ADF is -0.784587, significantly larger than the 1% level in the critical test value of -4.3260, the 5% level greater than the critical value of -3.2195 testing, but also greater than 10% level in the critical test value -2.7557, so the total population of the data sequence logx Xt is a non-stationary series.Table 3 National energy consumption (kg of standard coal) unit root test logyObtained from Table 3: National energy consumption (kg of standard coal) data Yt of the ADF is 0.489677, significantly larger than the 1% level in the critical test value of -4.3260, the 5% level greater than the critical test value of -3.2195, but also 10% greater than the critical level test value -2.7557, so the total population of the sequence logx data Yt is a non-stationary series.(3) Sequence of differentialBecause of the number of time series after still not a smooth sequence, so the need for further logarithm of the total population after the sequence logx and after a few of the universal life energy consumption (kg of standard coal) differential sequence data logY differential sequences were recorded as logx and logy. Are respectively the second-order differential of the total population of the sequence logX and second-order differential of the national energy consumption (kg of standard coal) sequence data logy the ADF unit root test (Table 7 and Table 8), test results the following table.Table 4Table 4 shows that the total population of second-order differential sequence logx ADF value is -10.6278, apparently less than 1% level in the critical test value of -6.292057, less than the 5% level in the critical test value -4.450425 also 10% less than the level in the critical test value of -3.701534, second-order differential of the total population of the sequence logx is a stationary sequence.Table5 5Table 5 shows that the second-order differential universal life energy consumption (kg of standard coal) logy of the ADF is -6.395029, apparently less than 1% level in the critical test value of -4.4613, less than the 5 % level of the critical test value of -3.2695, but also less than the 10% level the critical value of -2.7822 testing, universal life, second-order differential consumption of energy (kg of standard coal) logy is a stationary sequence.3. Cointegration(1)Cointegration regressionCointegration theory in the 1980s there Engle Granger put forward specific, it is from the analysis of non-stationary time series start to explore the non-stationary variable contains the long-run equilibrium relationship between the non-stationary time series modeling provides a new solution.As the population time series Xt and universal life energy consumption time series Yt are logarithmic, the total population obtained by the analysis of time series logX and universal life energy consumption time series logY are second-order single whole sequence, so they may exist cointegration relationship. The results obtained by Eviews software operation is as follows:Table 6Obtained from Table 6:D(LNE2)= -0.054819 101.8623D(LOGX2) t = (-1.069855) (-1.120827)R2=0.122487 DW=1.593055（2）Check the smoothness of the residual sequenceFrom the Eviews software, get residual sequence analysis:Table 7 Residual series unit root testObtained from Table 7: second-order differential value of -5.977460 ADF residuals, significantly less than 1% level in the critical test value -4.6405, less than 5% level in the critical test value of -3.3350, but also less than 10% level in the critical test value of -2.8169. Therefore, the second-order difference of the residual et is a stationary time series sequence. Expressed as follows:D（ET,2）=-0.042260-1.707007D(ET(-1),2) t = （-0.783744） (-5.977460)DW= 1.603022 EG=-5.977460，Since EG =- 5.977460, check the AFG cointegration test critical value table (N = 2, = 0.05, T = 16) received, EG value is less than the critical value, so to accept the original sequence et is stationary assumption. So you can determine the total population and energy consumption of all the people living there are two variables are long-term cointegration relationship.4. ECM model to establishThrough the above analysis, after the second-order differential of the logarithm of the total population time series logX and second-order differential of Logarithm of of national energy consumption time series logY is a stationary sequence, the second-order differential residuals et is also a stationary series. So that the number of second-order differential of the national energy consumption time series logY as the dependent variable, after the second-order differential of the logarithm of the total population time series logX and second-order differential as residuals et from variable regression estimation, using Eviews software, the following findings:Table 8 ECM model resultsTable 8 can be written by the ECM standard regression model, results are as follows:D(logY2)= -0.047266-154.4568D(LNP2) +0.171676D(ET2) t = (-1.469685) (-2.528562) (1.755694)R2= 0.579628 DW=1.760658ECM regression equation of the regression coefficients by a significance test, the error correction coefficient is positive, in line with forward correction mechanism. The estimation results show that the province of everyones life changes in energy consumption depends not only on the change of the total population, but also on the previous years total population deviation from the equilibrium level. In addition, the regression results show that short-term changes in the total population of all the people living there is a positive impact on energy consumption. Because short-term adjustment coefficient is significant, it shows that all the people living in Jilin Province annual consumption of energy in its long-run equilibrium value is the deviation can be corrected well.5. ARMA model(1) Model to identifyAfter differential differenced stationary series into stationary time series, after the analysis can be used ARMR model, the choice of using the model of everyones life before the first stable after the annual energy consumption time series logY to estimate the first full life energy consumption sequence logY do autocorrelation and partial autocorrelation, the results of the following:Table 9 logy of the autocorrelation and partial autocorrelation mapObtained from Table 9, the relevant figure from behind, after K = 1 in a random interval, partial autocorrelation can be seen in K = 1 after a random interval. So we can live on national energy consumption to establish the sequence logY ARMA (1,1) model, following on the ARMA (1,1) model parameter estimation, which results in the following table:Table 10 ARMA (1,1) model parameter estimationTable 10 obtained by the ARMA (1,1) model parameter estimation is given by:D(LNE,2)=0.014184+0.008803D(LNE,2)t-1-0.858461Ut-1(2) ARMA (1,2) model testModel of the residuals obtained for white noise test, if the residuals are not white noise sequence, then the need for ARMA (1,2) model for further improvement; if it is white noise process, the acceptance of the original model. ARMA (1,2) model residuals test results are as follows:Table11 ARMA (1,2) model residuals testTable 11 shows, Q statistic P value greater than 0.05, so the ARMA (1,1) model, the residual series is white noise sequence and accept the ARMA (1,1) model. Our whole life to predict changes in energy consumption, the results are as follows:Figure 5 National energy consumption forecast mapJilin Province of everyones life through the forecast energy consumption, we can see all the people living consumption of energy is rising every year, which also shows that in the future for many years, Jilin Province, universal life energy consumption will be showing an upward trend. And because of the total population and the existence of universal life energy consumption effects of changes in the same direction, so the total population over the next many years, will continue to increase.6. ProblemsBased on the provinces total population and the national energy consumption cointegration analysis of the relationship between population and energy consumption obtained between Jilin Province, there are long-term stability of the interaction and mutual promotion of the long-run equilibrium relationship. The above analysis can be more accurate understanding of the energy consumption of Jilin Province, Jilin Province put forward a better proposal on energy conservation. Moment, Jilin Province facing energy problems:(1) The heavy industry still accounts for a large proportion of; (2)The scale of energy-intensive industry, the rapid growth of production of energy saving effect; (3)The coal-based energy consumption is still.7.Recommendation:(1) Population control, and actively cooperate with the national policy of family planning, ease the pressure on the average population can consume. (2) Raise awareness of the importance of energy saving, the implementation of energy-saving target responsibility system, energy efficiency are implemented. Conscientiously implement the State Council issued the statistics of energy saving, monitoring and evaluation program of the three systems. Strict accountabi