本文選題：系統(tǒng)突變 + 經(jīng)濟預(yù)測��； 參考：《南京航空航天大學(xué)》2013年碩士論文

【摘要】：突變現(xiàn)象是系統(tǒng)發(fā)展過程中的一種不連續(xù)、不平滑現(xiàn)象，這種現(xiàn)象在經(jīng)濟系統(tǒng)里時有發(fā)生。突變現(xiàn)象為平穩(wěn)運行的經(jīng)濟系統(tǒng)帶來極大的擾動，對此進行預(yù)測、預(yù)警意義重大。突變現(xiàn)象發(fā)生后，系統(tǒng)運行環(huán)境復(fù)雜多變，準(zhǔn)確把握系統(tǒng)運行趨勢很有必要。本文基于傳統(tǒng)突變理論、多元統(tǒng)計分析理論、灰數(shù)代數(shù)理論、貝葉斯推理技術(shù)和灰色預(yù)測理論，重點研究了經(jīng)濟系統(tǒng)中突變預(yù)警建模問題以及突變發(fā)生后的經(jīng)濟發(fā)展趨勢預(yù)測問題。論文的主要創(chuàng)新點如下： （1）建立了經(jīng)濟灰色突變預(yù)警組合模型，該模型直觀、動態(tài)、定量的預(yù)警經(jīng)濟系統(tǒng)的突變行為。論文給出了利用突變理論解決經(jīng)濟突變問題的統(tǒng)一建�？蚣芎土鞒蹋茉O(shè)了一條社會科學(xué)和突變理論之間的橋梁，克服了突變理論難以在經(jīng)濟問題應(yīng)用的難題，，進一步驗證了突變理論解決社會科學(xué)問題的可行性。 （2）構(gòu)建了經(jīng)濟突變發(fā)生后的灰色泛函預(yù)測GFAM(1,1)模型。針對系統(tǒng)發(fā)生突變或變革的當(dāng)期或短期內(nèi)，由于預(yù)測所需要的基本樣本數(shù)據(jù)量無法滿足要求，許多經(jīng)典預(yù)測模型失靈問題，本節(jié)將泛函理論與灰數(shù)代數(shù)理論相結(jié)合，并運用區(qū)間灰數(shù)的代數(shù)表征技術(shù)和Bayes網(wǎng)絡(luò)推理技術(shù)，建立了基于系統(tǒng)突變分析的灰色泛函預(yù)測GFAM(1,1)（Gray Function Analysis Model(1,1)）模型，模型充分挖掘和利用系統(tǒng)突變當(dāng)前時段或突變后較短時間內(nèi)的信息，以克服傳統(tǒng)模型必須在獲得足夠統(tǒng)計數(shù)據(jù)后才能進行預(yù)測的滯后性缺陷，在系統(tǒng)突發(fā)環(huán)境下實現(xiàn)科學(xué)的推理與預(yù)測。之后論證了模型最小二乘估計參數(shù)列定理和預(yù)測值定理，給出了利用GFAM(1,1)模型預(yù)測的步驟。 （3）利用突變預(yù)警模型系統(tǒng)分析了我國房地產(chǎn)市場突變預(yù)警、預(yù)測問題。旨在通過系統(tǒng)分析我國的房地產(chǎn)行業(yè)現(xiàn)有歷史數(shù)據(jù)，結(jié)合主成分分析、多元數(shù)據(jù)擬合等多元統(tǒng)計分析技術(shù)和突變理論，建立一個房地產(chǎn)行業(yè)的突變預(yù)警系統(tǒng)模型。模型直觀、動態(tài)、系統(tǒng)的反映房地產(chǎn)業(yè)歷史數(shù)據(jù)及當(dāng)前趨勢，實時預(yù)警房地產(chǎn)可能發(fā)生的系統(tǒng)性突變，為政府針對房地產(chǎn)業(yè)的宏觀經(jīng)濟調(diào)控提供政策性建議，為維護我國房地產(chǎn)業(yè)的平穩(wěn)、健康發(fā)展提供理論支持。
[Abstract]:Sudden change is a discontinuous and uneven phenomenon in the process of system development, which occurs from time to time in the economic system. The sudden change brings great disturbance to the smooth running economic system, and it is of great significance to predict it. After the sudden change, the operating environment of the system is complex and changeable, so it is necessary to grasp the running trend of the system accurately. This paper is based on the traditional catastrophe theory, multivariate statistical analysis theory, grey number algebra theory, Bayesian reasoning technology and grey prediction theory. This paper focuses on the modeling of catastrophe early warning in economic system and the prediction of economic development trend after the sudden change occurs. The main innovations of this paper are as follows: (1) A combination model of economic grey catastrophe warning is established, which is intuitive, dynamic and quantitative. This paper presents a unified modeling framework and flow chart to solve the problem of economic catastrophe by using catastrophe theory, builds a bridge between social science and catastrophe theory, and overcomes the difficult problem that catastrophe theory is difficult to apply in economic problems. Furthermore, the feasibility of catastrophe theory to solve social science problems is verified. (2) A grey functional prediction model of GFAM1 / 1) is constructed after the occurrence of economic catastrophe. In view of the problem of failure of many classical prediction models, the functional theory and grey algebraic theory are combined in this section because the basic sample data amount needed for forecasting cannot meet the requirements of the current or short period of system mutation or change. Using the algebraic representation of interval grey numbers and Bayesian network reasoning technology, the grey functional prediction model of GFAMU 1 / 1 Analysis model based on system mutation analysis is established. The model fully exploits and utilizes the information of the current period or the short time after the sudden change of the system, in order to overcome the lag defect that the traditional model has to obtain enough statistical data before it can be predicted. Scientific reasoning and prediction are realized in the system burst environment. Then, the parameter sequence theorem and forecast value theorem of model least square estimation are proved, and the steps of model prediction using GFAM-1) model are given. (3) the catastrophe early warning model is used to analyze the problem of sudden change early warning and prediction of real estate market in China. The purpose of this paper is to systematically analyze the existing historical data of the real estate industry in China, and to establish a catastrophe warning system model of the real estate industry by combining the principal component analysis (PCA), the multivariate data fitting and the catastrophe theory. The model is intuitionistic, dynamic, and systematically reflects the historical data and current trend of real estate industry. It can warn the real estate industry of the possible systemic mutation in real estate in real time, and provide policy advice for the government to adjust and control the real estate industry macroeconomy. In order to maintain the stability of China's real estate industry, the healthy development of theoretical support.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：F224;F299.23

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