The Combination Of Forecasting Model Based On Kernel Regression And Using It In China Coal Consumption Prediction

Coal energy system in China is one of the most important material basis, coalconsumption prediction on China's macro-economic health has an important role.Scientific prediction of coal consumption contribute to rational and effectivearrangements for national production. With the deepening of China's economicdevelopment and industrialization, increasing demand for coal, growing contradictionbetween supply and demand and, therefore, take advantage of scientific researchmethods, in China in the next few years in the field of coal consumption prediction willpromote economic progress.In field of coal consumption prediction, prediction model of many items, whilescholars are for single prediction method for continuous improvement, so that you canbetter characterization, simulation and forecasting. At present, the grey forecastingmethod of coal consumption prediction is usually used, method of regression analysis,trend prediction method and ARMA predictive model, but these methods due to theirown shortcomings in certain deficiencies in the forecast. So no single model has its ownadvantages and disadvantages, there is no perfect prediction model, and predict complex,changes in data trends showed strong random fluctuations, so in reality the prediction,prediction of deviation of the results will be compared with the actual data.The combination forecast method overcome has single forecast method ofinsufficient and limitations, because single forecast method often focused on of is a areaand ignored has other useful of information, combination forecast method is willdifferent of single forecast model build-up in a specific environment in the, by must oftechnology means gives all single forecast model different of weight coefficient, so canfull, and science, and effective to for forecast, reached improve forecast precision ofpurpose.Currently combination forecast method of type range, and pieces be lift, whichoptimal combination forecast is current research of a important content, but optimal combination forecast exists with weight coefficient may for negative of limitations, andnon-negative weight combination forecast of although avoid has weight for negative ofpossibilities, but empirical effect is than optimal combination forecast, to mitigation thisboth of contradictions, so article made has based on features vector extraction of nuclearregression combination forecast method, Efforts in looking for the basis of optimalcombination forecasting of weighting coefficients are positive. Empirical sectionbetween1958and historical consumption data of coal to the coal consumption in China:an empirical study, through comparison with other methods to forecast that thecombined forecast methods present certain advantages.To description based on nuclear regression combination forecast model moreapplies Yu in China coal of consumption forecast, article first analysis has combinationforecast research area in the of research background and the significance, then moredescribes common coal consumption of single forecast model nature and limitations,last made based on features vector extraction of nuclear regression combination forecastmodel, and in empirical research in the came based on nuclear regression combinationforecast model of forecast results better than other forecast model, so in coalconsumption forecast in the nuclear regression combination forecast model has morestrong of adaptability.