本文關(guān)鍵詞： 經(jīng)濟(jì)時(shí)間序列 ARIMA模型 支持向量機(jī) 混合模型　出處：《首都經(jīng)濟(jì)貿(mào)易大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：經(jīng)濟(jì)時(shí)間序列數(shù)據(jù)反映了經(jīng)濟(jì)各個(gè)方面的運(yùn)行狀況，，對(duì)其進(jìn)行正確分析具有重要的意義。經(jīng)濟(jì)時(shí)間序列具有不穩(wěn)定、復(fù)雜和難以預(yù)測(cè)的特征，分析方法包括傳統(tǒng)的計(jì)量經(jīng)濟(jì)學(xué)方法，統(tǒng)計(jì)學(xué)方法和近幾年發(fā)展起來的機(jī)器學(xué)習(xí)方法，這些方法都具有一定的優(yōu)點(diǎn)，也存在一些不足，計(jì)量經(jīng)濟(jì)學(xué)方法對(duì)假設(shè)條件的要求比較嚴(yán)苛，神經(jīng)網(wǎng)絡(luò)等方法容易發(fā)生過擬合等問題。 為了進(jìn)一步提高趨勢(shì)分析的準(zhǔn)確性，本文將計(jì)量經(jīng)濟(jì)學(xué)中的ARIMA模型與支持向量機(jī)模型相結(jié)合，由于ARIMA模型在線性時(shí)間序列的分析中有較高的準(zhǔn)確性，而支持向量機(jī)模型在非線性時(shí)間序列建模上具有較好的能力，所以將兩者結(jié)合，形成混合模型，利用混合模型對(duì)經(jīng)濟(jì)時(shí)間序列進(jìn)行分析。 本文首先選取社會(huì)消費(fèi)品零售總額的時(shí)間序列進(jìn)行實(shí)證研究，社會(huì)消費(fèi)品零售總額反映社會(huì)商品購買力的實(shí)現(xiàn)程度和零售市場(chǎng)的規(guī)模狀況。在計(jì)量經(jīng)濟(jì)學(xué)建模時(shí)，根據(jù)其擾動(dòng)項(xiàng)同方差的特點(diǎn)選擇了ARIMA模型，再利用支持向量機(jī)模型對(duì)其進(jìn)行研究，最后，引入ARIMA與支持向量機(jī)混合模型，將ARIMA模型得到的估測(cè)值與支持向量機(jī)模型對(duì)殘差建模得到的估測(cè)值相加，得到混合模型結(jié)果。對(duì)三種模型的結(jié)果進(jìn)行比較，驗(yàn)證混合模型的效果最佳。 為了驗(yàn)證混合模型的準(zhǔn)確性和應(yīng)用范圍，本文還選取了上證綜合指數(shù)的時(shí)間序列進(jìn)行分析，同樣采取三種模型進(jìn)行分析研究。最后得出結(jié)論：混合模型具有很好的擬合效果和估測(cè)精度。
[Abstract]:The data of economic time series reflect the operation of various aspects of economy, and it is of great significance to analyze it correctly. Economic time series are characterized by instability, complexity and unpredictability. The analytical methods include traditional econometric methods, statistical methods and machine learning methods developed in recent years. The requirements of econometrics methods on the assumption conditions are strict, and the neural network methods are prone to the problems of over-fitting and so on. In order to further improve the accuracy of trend analysis, this paper combines the ARIMA model in econometrics with the support vector machine model, because the ARIMA model has a high accuracy in the analysis of linear time series. The support vector machine (SVM) model has a good capability in nonlinear time series modeling, so it combines the two models to form a hybrid model and uses the hybrid model to analyze the economic time series. This paper first selects the time series of total retail sales of consumer goods to conduct an empirical study. The total amount of retail sales of consumer goods reflects the degree of realization of purchasing power of social goods and the scale of retail market. According to the characteristic of the same square difference of the disturbance term, the ARIMA model is selected, and then the support vector machine model is used to study it. Finally, the mixed model of ARIMA and support vector machine is introduced. The results of mixed model are obtained by adding the estimated values of ARIMA model and support vector machine model to the residual model, and the results of the three models are compared to verify the best effect of the hybrid model. In order to verify the accuracy and application range of the hybrid model, this paper also selects the time series of Shanghai Composite Index to analyze. Finally, the conclusion is drawn that the mixed model has good fitting effect and estimation accuracy.
【學(xué)位授予單位】：首都經(jīng)濟(jì)貿(mào)易大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F224;F832.51;F726

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 雷強(qiáng);郭白瀅;;BDS替代數(shù)據(jù)法的人民幣匯率非線性特征研究[J];管理學(xué)報(bào);2010年02期

2 翁小清;沈鈞毅;;基于滑動(dòng)窗口的多變量時(shí)間序列異常數(shù)據(jù)的挖掘[J];計(jì)算機(jī)工程;2007年12期

3 李興緒,崔建福;MATLAB在金融時(shí)間序列分析及建模中的應(yīng)用[J];計(jì)算機(jī)工程與科學(xué);2004年07期

4 李方方;趙英凱;顏昕;;基于Matlab的最小二乘支持向量機(jī)的工具箱及其應(yīng)用[J];計(jì)算機(jī)應(yīng)用;2006年S2期

5 李峰;肖建華;;時(shí)間序列相似性分析中滑動(dòng)窗口寬度的確定[J];計(jì)算機(jī)科學(xué)與探索;2009年01期

6 張燕;楊洋;;基于小波分析的金融時(shí)間序列消噪方法及應(yīng)用[J];寧波大學(xué)學(xué)報(bào)(理工版);2010年03期

7 萬建強(qiáng),文洲;ARIMA模型與ARCH模型在香港股指預(yù)測(cè)方面的應(yīng)用比較[J];數(shù)理統(tǒng)計(jì)與管理;2001年06期

8 畢星;王巍;;基于經(jīng)驗(yàn)?zāi)Ｊ椒纸夂鸵苿?dòng)平均的金融時(shí)間序列分析[J];天津大學(xué)學(xué)報(bào)(社會(huì)科學(xué)版);2010年02期

9 王玉榮;ARIMA模型在我國(guó)出口貿(mào)易預(yù)測(cè)中的應(yīng)用[J];統(tǒng)計(jì)與決策;2004年04期

10 何星星;孫德山;;模糊神經(jīng)網(wǎng)絡(luò)與SARIMA結(jié)合的時(shí)間序列預(yù)測(cè)模型[J];計(jì)算機(jī)技術(shù)與發(fā)展;2008年08期

相關(guān)博士學(xué)位論文 前2條

1 汪東;基于支持向量機(jī)的選時(shí)和選股研究[D];上海交通大學(xué);2007年

2 丁暉;基于神經(jīng)網(wǎng)絡(luò)模型的人民幣匯率預(yù)測(cè)研究[D];湖南大學(xué);2008年

相關(guān)碩士學(xué)位論文 前1條

1 錢吉夫;SVR季節(jié)性時(shí)間序列預(yù)測(cè)模型的構(gòu)建與應(yīng)用[D];華南理工大學(xué);2010年

本文編號(hào)：1545492