本文選題：時(shí)間序列ARIMA模型 + 灰色GM(1��； 參考：《蘭州交通大學(xué)》2016年碩士論文

【摘要】：居民消費(fèi)價(jià)格指數(shù),英文名稱consumer price index,即簡(jiǎn)稱CPI,是普遍編制的一種指數(shù),它可以用于分析市場(chǎng)價(jià)格的基本動(dòng)態(tài),為我國(guó)政府制定政策,對(duì)經(jīng)濟(jì)的宏觀調(diào)控提供重要參考依據(jù)。為了能更精確地把握CPI的走勢(shì),提供分析依據(jù),本文著重利用了一種組合模型—時(shí)間序列與灰色預(yù)測(cè)模型的組合,來(lái)對(duì)CPI進(jìn)行預(yù)測(cè)。由于現(xiàn)代科技的不斷進(jìn)步與發(fā)展,各種預(yù)測(cè)方法隨之與時(shí)俱進(jìn),因此發(fā)展了很多預(yù)測(cè)方法,就拿預(yù)測(cè)CPI的方法來(lái)說(shuō),就有很多,如時(shí)間序列模型、灰色模型、BP網(wǎng)絡(luò)神經(jīng)等方法層出不窮。但每個(gè)模型都有自己優(yōu)勢(shì)所在,當(dāng)然也有它自己不可避免的缺點(diǎn),所以為能縮小預(yù)測(cè)值與實(shí)際值得誤差,使預(yù)測(cè)值的可信度更高些,本文將基于有效利用各種單一模型的優(yōu)點(diǎn),把不同模型的計(jì)算結(jié)果綜合起來(lái),根據(jù)誤差大小分配單個(gè)模型在組合模型中所占的權(quán)重系數(shù),相互取長(zhǎng)補(bǔ)短,來(lái)彌補(bǔ)各種單個(gè)模型的缺點(diǎn)。本文選擇兩種模型進(jìn)行組合,即在基于時(shí)間序列的組合模型分析方法的基礎(chǔ)上,對(duì)我國(guó)的CPI進(jìn)行建模預(yù)測(cè)。本文首先著重詳細(xì)介紹了時(shí)間序列相關(guān)理論知識(shí),緊接著運(yùn)用這些理論對(duì)我國(guó)2013年5月—2015年4月CPI的月度數(shù)據(jù)對(duì)其進(jìn)行建立模型,其次介紹灰色預(yù)測(cè)模型的建模理論,因?yàn)榛疑A(yù)測(cè)模型需使用小樣本數(shù)據(jù),所以只選取了2014年7月—2015年4月的數(shù)據(jù),建立模型并進(jìn)行短期預(yù)測(cè)。建立單一的模型并通過(guò)檢驗(yàn)后,然后,求得單個(gè)模型的絕對(duì)誤差值,對(duì)2014年7月—2015年4月,分別求兩個(gè)模型的絕對(duì)誤差的平方和,利用方差倒數(shù)的方法算出兩個(gè)模型在組合模型的分別所占的權(quán)重系數(shù),也就是根據(jù)誤差大小建立組合模型,誤差平方和大的模型所占權(quán)值較小,相反,誤差平方和較小者,其所占權(quán)值反而大。除了用數(shù)據(jù)說(shuō)明組合模型的優(yōu)勢(shì)有降低預(yù)測(cè)誤差的偏差大小以及波動(dòng)幅度外,文中還用理論證實(shí)了此優(yōu)勢(shì)。最后對(duì)我國(guó)2005年—2014年的CPI數(shù)據(jù)建立基于時(shí)間序列的組合模型,并預(yù)測(cè)出2015年、2016年的CPI數(shù)據(jù),預(yù)測(cè)結(jié)果表明,我國(guó)近兩年的CPI較穩(wěn)定,在政府制定政策時(shí),進(jìn)獻(xiàn)上微薄之力。
[Abstract]:The consumer price index, the English name consumer price index, or CPI, is a general index. It can be used to analyze the basic dynamics of the market price. It provides an important reference for our government to formulate policies and to provide an important reference for the macroeconomic regulation and control. In order to more accurately grasp the trend of CPI, this paper provides an analysis basis. This article focuses on this paper. This article focuses on this paper. The combination of a combination model, time series and grey prediction model, is used to predict CPI. Because of the continuous progress and development of modern science and technology, various forecasting methods are progressing with the times, so many forecasting methods have been developed. There are many methods for predicting CPI, such as time series model, grey model, BP network God. Each model has its own advantages, but each model has its own advantages, and of course it has its own unavoidable shortcomings. So, in order to reduce the prediction value and the reality, the reliability of the prediction value is higher. This article will be based on the advantages of the effective use of a variety of models, the results of different models are integrated, according to the error. The weight coefficients of a single model in the combination model are complementary to each other to make up for the shortcomings of a variety of individual models. In this paper, two models are combined, that is, on the basis of the combination model analysis method based on time series, the modeling and prediction of CPI in China are carried out. The theory of sequence related theory is followed by using these theories to model the monthly data of CPI in China from May 2013 to April 2015. Secondly, the model theory of grey prediction model is introduced, because the grey prediction model needs small sample data, so only the data from July 2014 to April 2015 are selected and the model is established and carried out. A single model is established and the absolute error value of a single model is obtained by establishing a single model. The sum of the absolute error of the two models is calculated from July 2014 to April 2015. The weight coefficients of the two models in the combined model are calculated by the method of the reciprocal of variance, which is based on the size of the error. In combination, the weight of the square error and the large model is smaller. On the contrary, the weight of the small error square sum is larger. In addition to the advantage of the combination model with the data to reduce the deviation of the prediction error and the amplitude of the fluctuation, the advantage is confirmed by the theory. Finally, the CPI data of China from 2005 to 2014. A combination model based on time series is set up and the CPI data of 2015 and 2016 are predicted. The forecast results show that our country's CPI is more stable in the last two years.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：F224;F726

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