本文關(guān)鍵詞： 私人汽車擁有量 時(shí)間序列 偏最小二乘 動(dòng)態(tài)組合　出處：《四川師范大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：1985年,我國(guó)私人汽車擁有量?jī)H有28.49萬(wàn)輛,經(jīng)過(guò)三十年的發(fā)展,到2015年底,我國(guó)私人汽車擁有量已超過(guò)14000萬(wàn)輛。在可預(yù)見(jiàn)的未來(lái),私人汽車擁有量也將保持高速、持續(xù)增長(zhǎng)。但是,私人汽車在方便人們出行的時(shí)候,也帶來(lái)了一系列亟待解決的社會(huì)問(wèn)題,比如日益嚴(yán)重的交通擁堵、空氣污染等問(wèn)題。因此,預(yù)測(cè)我國(guó)私人汽車擁有量及其變化趨勢(shì),已經(jīng)成為研究的熱點(diǎn)�？茖W(xué)準(zhǔn)確的預(yù)測(cè)私人汽車擁有量,能為國(guó)家決策部門做出相應(yīng)的產(chǎn)業(yè)政策調(diào)整、供給側(cè)改革提供理論基礎(chǔ)和數(shù)據(jù)支撐,同時(shí)也能夠?yàn)槠囆袠I(yè)的規(guī)劃發(fā)展方向提供依據(jù)。本文圍繞我國(guó)私人汽車擁有量及其主要影響因素展開(kāi)研究。研究的目的是通過(guò)建立動(dòng)態(tài)組合模型來(lái)預(yù)測(cè)我國(guó)私人汽車的擁有量及其發(fā)展趨勢(shì),并以此為出發(fā)點(diǎn),提出合理的建議。為此,主要做了以下研究工作:首先,根據(jù)偏最小二乘回歸理論,建立私人汽車擁有量與其主要因素之間的回歸模型。同時(shí),通過(guò)對(duì)各影響因素進(jìn)行預(yù)測(cè),進(jìn)而利用偏最小二乘回歸模型對(duì)私人汽車擁有量做出了預(yù)測(cè)。其次,通過(guò)對(duì)私人汽車擁有量的時(shí)序數(shù)據(jù)的分析,建立時(shí)間序列季節(jié)模型,并利用季節(jié)模型預(yù)測(cè)私人汽車的擁有量。最后,以回歸組合預(yù)測(cè)法來(lái)確定組合權(quán)重,同時(shí),考慮到單項(xiàng)預(yù)測(cè)模型可能會(huì)隨著時(shí)間的變化,預(yù)測(cè)效果將會(huì)發(fā)生改變,因此,加入了時(shí)間參數(shù),對(duì)單項(xiàng)預(yù)測(cè)模型進(jìn)行修正,采用可變加權(quán)系數(shù),建立動(dòng)態(tài)組合預(yù)測(cè)模型,從而對(duì)我國(guó)私人汽車未來(lái)發(fā)展做出更合理的預(yù)測(cè)。本文的貢獻(xiàn)在于:一方面,在分析私人汽車擁有量與其影響因素之間的關(guān)系時(shí),采用了偏最小二乘回歸方法。該方法在提取成分,在有效地解決影響因素之間多重共線性問(wèn)題的同時(shí),又最大限度地使提取的成分與私人汽車擁有量有關(guān)。另一面,考慮到單項(xiàng)預(yù)測(cè)模型可能會(huì)隨著時(shí)間的變化,預(yù)測(cè)效果將會(huì)發(fā)生改變,因此,增加了時(shí)間參數(shù)對(duì)單項(xiàng)預(yù)測(cè)模型進(jìn)行修正,從而建立的動(dòng)態(tài)組合預(yù)測(cè)模型,在實(shí)際研究中更具有研究和運(yùn)用價(jià)值。
[Abstract]:In 1985, there were only 284,900 private cars in our country. After 30 years of development, by end of 2015, the number of private cars in our country had exceeded 140 million. In the foreseeable future, private car ownership will also remain at a high speed. However, when private cars are convenient for people to travel, they also bring about a series of social problems that need to be solved urgently, such as increasing traffic congestion, air pollution and so on. Forecasting private car ownership and its changing trend in China has become a hot topic of research. Scientific and accurate prediction of private car ownership can make corresponding industrial policy adjustments for national policy-making departments. The supply-side reform provides the theoretical basis and data support, At the same time, it can also provide the basis for the planning and development direction of automobile industry. The purpose of this study is to establish a dynamic combination model to predict China's private car ownership and its main influencing factors. The ownership of people and cars and its development trend, Based on the partial least square regression theory, the regression model between the ownership of private cars and its main factors is established. At the same time, the following research work is done: first, according to the partial least squares regression theory, a regression model between the ownership of private cars and its main factors is established. Through the prediction of the influencing factors, the partial least square regression model is used to predict the ownership of private cars. Secondly, through the analysis of the time series data of the ownership of private cars, the seasonal model of time series is established. Finally, the regression combination forecasting method is used to determine the combination weight, and considering that the single prediction model may change over time, the forecasting effect will change. The time parameter is added, the single prediction model is modified, the variable weighting coefficient is adopted, and the dynamic combination forecasting model is established, which makes a more reasonable prediction for the future development of private cars in China. The contributions of this paper are as follows: on the one hand, The partial least square regression method is used to analyze the relationship between private car ownership and its influencing factors. On the other hand, considering that the single prediction model may change over time, the prediction effect will change. The time parameter is added to modify the single prediction model, and the dynamic combination prediction model is established, which has more research and application value in the actual research.
【學(xué)位授予單位】：四川師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：O212.1

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