【摘要】：支持向量機作為一種常見的數(shù)據(jù)挖掘方法,相對其他方法來說具有十分突出的優(yōu)點,目前在各個領域的應用也十分廣泛。但是,該方法在實際運用中還有許多問題值得研究,模型本身也有很多可以改進的地方。為了進一步地改進支持向量機模型并對其進行推廣,本文選取了目前研究較多,但對支持向量機方法的運用上還存在問題的資本市場作為研究對象。本文針對目前的股市預測方法以及標準支持向量機存在的缺陷進行了分析,在此基礎上提出了結合粗糙集的最小二乘支持向量機模型對股市進行預測,首先應用粗糙集對預測指標進行屬性約簡,然后對約簡的指標體系使用最小二乘支持向量機來預測股價的波動情況,以期能夠給股市預測提供一定的參考,并對粗糙集與最小二乘支持向量機的應用提供思路與方法。本文的主要研究內(nèi)容為:首先系統(tǒng)地介紹了關于股票市場預測、支持向量機、最小二乘支持向量機和粗糙集的國內(nèi)外研究現(xiàn)狀,并在前人研究的基礎上總結了現(xiàn)有方法的不足,鑒于現(xiàn)有方法的不足,提出RS-LS-LSVM股市預測方法;其次對粗糙集和最小二乘支持向量機的基礎理論以及模型中涉及的核函數(shù)的選擇問題進行了說明;隨后根據(jù)模型的整體思路建立了模型的流程圖,詳細描述了該預測模型的處理過程,并建立了一套包括今日最高價、昨日最高價、前日最高價、7日平均最高價等在內(nèi)的27個指標構成的預測指標體系,并對模型中各個步驟的處理過程與方法進行了一一說明;最后在主板、中小板、創(chuàng)業(yè)板中隨機選取了中國石油(601857)、輝隆股份(002556)和勁勝精密(300083)的2016年全年的交易數(shù)據(jù)作為研究樣本,每個樣本都是244組數(shù)據(jù),在MATLAB軟件中進行了三次三組模型的對比實驗,每次對比實驗又進行了20次的隨機試驗,在軟件中分別使用RS-LS-SVM、LS-SVM和RS-SVM對樣本數(shù)據(jù)回歸預測,并對結果進行了對比。實驗結果表明:一、實驗針對三個樣本得到了三個不同的約簡指標體系,說明相同的指標在針對不同的預測對象時,有效指標不同;相同的指標對不同的預測對象的應用上也存在區(qū)別。所以預測之前進行指標篩選和屬性約簡是十分必要的,屬性約簡可以減少數(shù)據(jù)冗余,提高預測性能,并且本文提出的初選指標體系能夠在一定程度上對我國股市進行預測。二、實驗結果驗證了RS-LS-SVM預測模型的可行性和有效性。無論是在主板、中小板還是在創(chuàng)業(yè)板中,多次實驗結果表明,在MSE和RMSE兩個數(shù)據(jù)上表現(xiàn),RS-LS-SVM預測模型都比LS-SVM和RS-SVM模型更為優(yōu)秀�？梢钥闯�,將粗糙集與最小二乘支持向量機引入股票市場中,簡化了模型難度,提高了求解速度,具有一定的創(chuàng)新性,這對于股票市場預測模型的研究和投資者進行投資決策具有參考價值。
[Abstract]:As a common data mining method, support vector machine (SVM) has many advantages compared with other methods, and it is widely used in various fields. However, there are still many problems worth studying in practical application, and the model itself can be improved. In order to further improve the support vector machine (SVM) model and generalize it, this paper selects the capital market which has some problems in the application of support vector machine (SVM). Based on the analysis of the current stock market forecasting methods and the defects of the standard support vector machine (SVM), a rough set least squares support vector machine (LS-SVM) model is proposed to predict the stock market. First, the rough set is used to reduce the attribute of the prediction index, and then the least square support vector machine is used to predict the fluctuation of stock price in the index system of the reduction, so as to provide a certain reference for the stock market forecast. It also provides ideas and methods for the application of rough set and least squares support vector machine. The main contents of this paper are as follows: firstly, this paper systematically introduces the research status of stock market prediction, support vector machine, least square support vector machine and rough set, and summarizes the shortcomings of existing methods on the basis of previous studies. In view of the shortcomings of the existing methods, the RS-LS-LSVM stock market forecasting method is put forward. Secondly, the basic theory of rough set and least squares support vector machine (LS-SVM) and the selection of kernel function in the model are explained. Then, according to the overall idea of the model, the flow chart of the model is established, the processing process of the prediction model is described in detail, and a set of prices including the highest price today, the highest price yesterday, the highest price the day before yesterday, The prediction index system is composed of 27 indexes including the highest price of 7 days, and the processing process and method of each step in the model are explained one by one. Finally, in the main board, the small and medium-sized board, and the gem, the transaction data of PetroChina (601857), Huilong shares (002556) and Jinsheng Precision (300083) for the whole year 2016 were randomly selected as the research samples. Each sample was 244sets of data. The contrast experiments of three groups of models were carried out in MATLAB software, and 20 random experiments were carried out each time. In the software, RS-LS-SVM,LS-SVM and RS-SVM were used to predict the sample data respectively. The results are compared. The experimental results show that: first, three different reduction index systems are obtained for the three samples, which shows that the same index is different for different prediction objects; There are differences in the application of the same indicators to different prediction objects. Therefore, it is very necessary to carry out index selection and attribute reduction before prediction. Attribute reduction can reduce data redundancy and improve prediction performance, and the primary index system proposed in this paper can predict the stock market in China to a certain extent. Second, the experimental results verify the feasibility and validity of the RS-LS-SVM prediction model. The results of many experiments on the main board, the small and medium-sized board and the growth enterprise board show that the RS-LS-SVM prediction model is better than the LS-SVM and RS-SVM model in the data of MSE and RMSE. It can be seen that introducing rough set and least squares support vector machine into stock market simplifies the difficulty of the model and improves the speed of solution. It has reference value for the research of stock market forecasting model and investors' investment decision.
【學位授予單位】：成都理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F832.51

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本文編號：2395095