本文選題：聚類分析 + 房地產(chǎn)價(jià)格　； 參考：《長(zhǎng)春工業(yè)大學(xué)》2017年碩士論文

【摘要】：通過觀察和研究房地產(chǎn)價(jià)格波動(dòng)情況,人們能夠?qū)?guó)家經(jīng)濟(jì)的發(fā)展情況和房地產(chǎn)市場(chǎng)的變化進(jìn)行整體的把握,以便進(jìn)行有針對(duì)性的投資或交易行為。近年來,隨著我國(guó)房地產(chǎn)交易市場(chǎng)的活躍和快速升溫,行業(yè)領(lǐng)域的潛在風(fēng)險(xiǎn)愈加的突出,如何使用合理有效的方法對(duì)房地產(chǎn)市場(chǎng)和房屋價(jià)格的波動(dòng)情況進(jìn)行反映,并及時(shí)提供正確的預(yù)警和提示變得非常重要。考慮到上述問題,本文從實(shí)際情況出發(fā),對(duì)房地產(chǎn)市場(chǎng)價(jià)格的預(yù)測(cè)進(jìn)行了研究。首先使用基于聚類分析的統(tǒng)計(jì)學(xué)方法對(duì)房地產(chǎn)價(jià)格產(chǎn)生影響的主要因素進(jìn)行了分析和選擇,這種指標(biāo)選擇方法有效解決了模型預(yù)測(cè)的代表性和全面性相互平衡的問題。在確定評(píng)價(jià)指標(biāo)以后,我們基于國(guó)家統(tǒng)計(jì)年鑒和吉林省統(tǒng)計(jì)年鑒所公布的吉林省房地產(chǎn)數(shù)據(jù),應(yīng)用支持向量回歸算法對(duì)吉林省房屋價(jià)格的變化規(guī)律進(jìn)行了分析和預(yù)測(cè)。支持向量回歸模型具有較好的統(tǒng)計(jì)理論基礎(chǔ)和對(duì)少量數(shù)據(jù)情況下模型的較好擬合能力。然后,通過MATLAB軟件編程語言實(shí)現(xiàn)了支持向量回歸算法的構(gòu)建、分析和預(yù)測(cè)。最后通過對(duì)比支持向量回歸模型與RBF神經(jīng)網(wǎng)絡(luò)模型的預(yù)測(cè)誤差,從結(jié)果上表明了支持向量回歸模型的有效性和良好的預(yù)測(cè)精度。
[Abstract]:Through observing and studying the fluctuation of real estate price, people can grasp the development of national economy and the change of real estate market as a whole, in order to carry on the targeted investment or transaction behavior. In recent years, with the active and rapid warming of the real estate market in China, the potential risks in the field of the industry become increasingly prominent. How to use reasonable and effective methods to reflect the volatility of the real estate market and housing prices, It is important to provide the correct warning and warning in time. Considering the above problems, this paper studies the real estate market price forecasting from the actual situation. Firstly, the main factors influencing real estate prices are analyzed and selected by using the statistical method based on cluster analysis. This index selection method effectively solves the problems of representativeness and comprehensiveness of model prediction. Based on the real estate data of Jilin Province published in the National Statistical Yearbook and the Statistical Yearbook of Jilin Province, we apply the support vector regression algorithm to analyze and predict the changing law of housing prices in Jilin Province. The support vector regression model has a good statistical theoretical foundation and a good fit ability for the model with a small amount of data. Then, the support vector regression algorithm is constructed, analyzed and predicted by MATLAB software programming language. Finally, by comparing the prediction error between the support vector regression model and the RBF neural network model, the results show that the support vector regression model is effective and has good prediction accuracy.
【學(xué)位授予單位】：長(zhǎng)春工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F299.23

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本文編號(hào)：2056331