發(fā)布時間：2018-04-13 11:38

  本文選題：國際原油價格 + 核化極速神經(jīng)網(wǎng)絡(luò)　； 參考：《重慶工商大學》2015年碩士論文

【摘要】：石油是保障國民經(jīng)濟各部門順利運行的重要戰(zhàn)略物資。近十年來,國際原油價格的頻繁波動越來越成為制約各國經(jīng)濟平穩(wěn)運行的不穩(wěn)定因素。中國是全球最大的原油進口國,原油價格的波動對中國經(jīng)濟的穩(wěn)定運行造成了干擾。在變化莫測的世界原油市場中,若能對油價的走向作出正確的預(yù)測,這樣在面對油價的大幅波動時,可以將其造成的不利經(jīng)濟影響降到最低,使自身利益得到最大限度的維護。因此,密切關(guān)注國際原油市場,探索國際原油價格變動的潛在原因,對原油價格的走向進行合理的預(yù)測,對國家、企業(yè)和個人都具有重要的意義。鑒于原油具有商品、金融及政治等多種屬性,本文將WTI原油價格的月度數(shù)據(jù)作為研究對象,不僅考慮油價時間序列自身的發(fā)展規(guī)律,并且通過灰色關(guān)聯(lián)分析(GRA)篩選了影響原油價格的8個重要因素,提出了EMD_GA_KELM原油價格預(yù)測模型。本文的模型構(gòu)建主要體現(xiàn)以下兩個方面的工作:首先,核化極速神經(jīng)網(wǎng)絡(luò)(KELM)是借鑒支持向量機(SVM)核函數(shù)的原理對極速神經(jīng)網(wǎng)絡(luò)(ELM)的擴展,因此KELM模型同樣存在懲罰系數(shù)與核參數(shù)難以選取的問題,本文運用遺傳算法(GA)對KELM的懲罰因子和核參數(shù)進行優(yōu)化,建立GA_KELM預(yù)測模型,通過對WTI原油月度價格的實證分析表明,GA_KELM預(yù)測的均方誤差為0.05,其預(yù)測精度較沒有優(yōu)化的KELM模型和SVM模型分別提高了5.7%和16.75%;其次,本文將主要用于信號技術(shù)的經(jīng)驗?zāi)B(tài)分解方法(EMD)運用在非平穩(wěn)非線性的油價序列研究中,將原油價格序列分解成若干不同頻率的分量,單獨對每個分量運用GA_KELM模型進行預(yù)測,將每個分量的預(yù)測結(jié)果通過相加重構(gòu)的方式得到最終的原油價格預(yù)測值,實證結(jié)果表明,采用EMD_GA_KELM進行預(yù)測的效果要遠遠好于單獨采用GA_KELM模型預(yù)測的效果,相對誤差為0.041,預(yù)測精度提高了17.5%,這也說明了本文所使用的預(yù)測方法是可行的,可以作為未來原油價格預(yù)測的有效方法之一,對原油價格的預(yù)測具有較大參考意義。
[Abstract]:Petroleum is an important strategic material to ensure the smooth operation of various departments of the national economy.In recent ten years, the frequent fluctuation of international crude oil price has become an unstable factor restricting the smooth operation of economy.China is the world's largest importer of crude oil, and fluctuations in crude oil prices have interfered with the stable operation of China's economy.Therefore, it is of great significance for countries, enterprises and individuals to pay close attention to the international crude oil market, explore the potential reasons for the change of international crude oil prices, and make a reasonable prediction of the trend of crude oil prices.In view of the commodity, financial and political properties of crude oil, this paper takes the monthly data of WTI crude oil price as the research object, not only considering the development law of oil price time series itself.Eight important factors affecting crude oil price were screened by grey relational analysis (gra), and the EMD_GA_KELM crude oil price prediction model was put forward.The model construction of this paper mainly embodies the following two aspects of work: firstly, the nucleation extreme speed neural network (KELM) is an extension of the extreme speed neural network (ELM) based on the principle of support vector machine (SVM) kernel function.Therefore, the KELM model also has the problem that it is difficult to select the penalty coefficient and kernel parameter. In this paper, the penalty factor and kernel parameter of KELM are optimized by genetic algorithm (GA), and the prediction model of GA_KELM is established.The empirical analysis on the monthly price of WTI crude oil shows that the mean square error of Gackelm prediction is 0.05, and the prediction accuracy is 5.7% and 16.75% higher than that of the unoptimized KELM model and SVM model, respectively.In this paper, the empirical mode decomposition (EMD) method, which is mainly used in signal technology, is applied to the study of non-stationary and nonlinear oil price series. The price sequence of crude oil is decomposed into several components with different frequencies, and each component is predicted by GA_KELM model.The final crude oil price prediction value is obtained by adding and reconstructing the prediction results of each component. The empirical results show that the effect of EMD_GA_KELM prediction is much better than that of using GA_KELM model alone.The relative error is 0.041 and the prediction accuracy is improved by 17.50.This also shows that the prediction method used in this paper is feasible and can be used as one of the effective methods for predicting the price of crude oil in the future. It is of great reference significance for the prediction of crude oil price.
【學位授予單位】：重慶工商大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：F416.22;F764.1;F224

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