【作者】 王春宝；

【导师】 董景荣；

【作者基本信息】 重庆师范大学 ， 管理科学与工程， 2012， 硕士

【摘要】 煤炭是我国能源体系中最重要的物质基础之一，煤炭消费预测对我国的宏观经济健康发展有着重要作用。科学的煤炭消费预测有助于合理、有效地安排国民生产。随着我国经济的发展和工业化进程的深入，煤炭的需求量不断上升，供需之间矛盾日益加剧，因此，利用科学合理的研究方法，对今后数年我国在煤炭领域的消费数量进行预测会推动经济的不断进步。在煤炭消费预测领域中，存在许多的单项预测模型，同时学者们也在对单项预测方法进行不断的改进，从而可以地更好刻画、模拟和预测。目前，煤炭消费预测通常采用灰色预测法，回归分析法、趋势外推预测法以及ARMA预测模型等，但是这些方法由于自身的缺陷在预测中存在一定的不足。所以任何单一模型在都有它自身的优缺点，完美无缺的预测模型是不存在的，而预测对象错综复杂，数据变化的趋势表现出较强的随机波动性，所以在现实的预测研究中，预测的结果会与实际数据有比较大的偏差。组合预测方法克服了单一预测方法的不足与局限性，因为单一预测方法往往侧重的是一个方面而忽略了其他有用的信息，组合预测方法是将不同的单一预测模型集结在一个特定环境中，通过一定的技术手段赋予各个单一预测模型不同的权重系数，这样可以全面、科学、有效地进行预测，达到提高预测精度的目的。目前组合预测方法的类型繁多、枚不胜举，其中最优组合预测是当前研究的一个重要内容，但是最优组合预测存在着权重系数可能为负的局限性，而非负权重组合预测的虽然避免了权重为负的可能性，但是实证效果却不及最优组合预测，为了缓解这二者的矛盾，于是文章提出了基于特征向量提取的核回归组合预测方法，即努力在最优组合预测的基础上来寻找权重系数都为正的情况。实证部分将1958年至2010年我国煤炭的历史消费数据运用到煤炭消费的实证研究中，通过与其他方法预测进行比较得出该组合预测方法存在一定的优势。为了说明基于核回归组合预测模型更适用于我国煤炭的消费预测，文章先分析了组合预测研究领域中的研究背景及意义，接着详细介绍常用煤炭消费的单一预测模型性质与局限性，最后提出基于特征向量提取的核回归组合预测模型，并在实证研究中得出基于核回归组合预测模型的预测结果优于其他预测模型，故在煤炭消费预测中核回归组合预测模型具有更强的适应性。更多还原

【Abstract】 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.更多还原