本文選題：灰色預(yù)測模型 + 自助法��； 參考：《重慶大學(xué)》2014年碩士論文

【摘要】：隨著中國改革開放的發(fā)展，社會主義經(jīng)濟(jì)日趨完善，房地產(chǎn)業(yè)也呈現(xiàn)了一片欣欣向榮的景象。但是伴隨而來的房地產(chǎn)投機(jī)現(xiàn)象也不斷涌現(xiàn)，住宅房價(jià)增長迅猛，遠(yuǎn)遠(yuǎn)超出人民大眾的購買力，樓市的泡沫經(jīng)濟(jì)已經(jīng)嚴(yán)重影響國家經(jīng)濟(jì)的健康發(fā)展。 國家和政府開始在2010年對住宅房價(jià)進(jìn)行宏觀調(diào)控，至此，中國房地產(chǎn)政策已由此前的支持轉(zhuǎn)向抑制投機(jī)，為了遏制住宅房價(jià)過快上漲，國家還先后采取了土地、金融、稅收等多種調(diào)控手段。至今，住宅房價(jià)迅猛增長的勢頭得到了有效的遏制。本文以調(diào)控開始有效的2011年至今的季度數(shù)據(jù)為依托去預(yù)測重慶以后季度的住宅房價(jià)格。然而如何建立一種可靠的預(yù)測模型，能夠預(yù)測出參考價(jià)值高的住宅房價(jià)，仍是一個(gè)相對困難的問題。 由于2011年至今的季度數(shù)據(jù)量小，所以它不能用通常經(jīng)典的大樣本預(yù)測方法去預(yù)測。本文首先就首先用一種可以模擬未知分布、再抽樣統(tǒng)計(jì)的方法——自助法，對原始數(shù)據(jù)進(jìn)行抽樣，以達(dá)到增大樣本容量的目的。然后，本文再結(jié)合灰色預(yù)測模型（灰色GM(1,1)模型），介紹了一種可以對小樣本數(shù)據(jù)預(yù)測的灰自助模型。最后，闡述了一種帶參數(shù)的灰自助模型，通過遺傳算法（GA，GeneticAlgorithm）去修正參數(shù)得到一種能夠使得預(yù)測值的全局平方誤差最小的模型——基于遺傳算法的灰自助模型（GA灰自助模型）。一般情況下，灰色預(yù)測模型和GA灰自助模型只能給出一個(gè)預(yù)測范圍，然而這個(gè)預(yù)測范圍往往太大，只能說明模型的正確性，不能給消費(fèi)者一個(gè)具體的參考。但是消費(fèi)者往往需要一個(gè)具體的參考，本文通過引入新的選擇序列，，我們可以根據(jù)選擇序列最后兩個(gè)序列碼來判定預(yù)測值的取值，進(jìn)而得到預(yù)測值。
[Abstract]:With the development of China's reform and opening up, the socialist economy is becoming more and more perfect, and the real estate industry is also flourishing. But accompanied by the speculation in real estate is also emerging, housing prices are growing rapidly, far beyond the purchasing power of the public, the bubble economy of the property market has seriously affected the healthy development of the national economy. The state and the government began to macro-control housing prices in 2010. At this point, China's real estate policy has shifted from previous support to curbing speculation. In order to curb the excessive rise in housing prices, the state has also adopted land and finance successively. Taxation and other means of regulation and control. So far, the rapid growth of housing prices has been effectively contained. Based on the effective quarterly data from 2011 to present, this paper predicts the housing prices in Chongqing. However, how to establish a reliable prediction model to predict the housing prices with high reference value is still a relatively difficult problem. Because of the small amount of data in the quarter from 2011 to now, it can not be predicted by the classical large sample prediction method. In this paper, we first use a self-help method, which can simulate the unknown distribution and then sample statistics, to sample the raw data, so as to increase the sample size. Then, this paper introduces a grey self-help model which can be used to predict small sample data. Finally, a grey self-help model with parameters is presented. By modifying the parameters by genetic algorithm, a model with minimum global squared error of predicted value is obtained, which is a grey self-help model based on genetic algorithm (GA). In general, the grey prediction model and the GA grey self-help model can only give a prediction range, but this prediction range is often too large, which can only explain the correctness of the model and can not give consumers a specific reference. But consumers often need a specific reference. By introducing a new selection sequence, we can judge the predicted value according to the last two sequence codes of the selection sequence, and then get the predicted value.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F299.23;TP18

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