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  本文關(guān)鍵詞： 模糊信息�；� 隸屬函數(shù) 支持向量(回歸)機(jī) 上證指數(shù)　出處：《蘭州大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：大數(shù)據(jù)時代的到來,給數(shù)據(jù)挖掘帶來了新的挑戰(zhàn),如何在海量的數(shù)據(jù)中挖掘有用信息成為一大課題。模糊信息�；Ｐ屯ㄟ^將信息�；赡：Ｗ�,能夠大幅度降低信息的復(fù)雜度,簡化問題求解過程,減少運(yùn)算量。隸屬函數(shù)作為模糊�；s束規(guī)則直接影響信息�；Ч�。本文將基于不同隸屬函數(shù)的模糊信息�；Ｐ秃椭С窒蛄�(回歸)機(jī)模型結(jié)合,組建混合模型,對上證指數(shù)進(jìn)行回歸分析,尋找使混合模型預(yù)測效果最佳的隸屬函數(shù)。上證指數(shù)實(shí)證分析的結(jié)果表明,基于不同隸屬函數(shù)的混合模型對模糊粒子中值R的預(yù)測結(jié)果相同,其中基于非對稱拋物線型隸屬函數(shù)的混合模型的范圍預(yù)測結(jié)果精確度和可靠度最高。然而,常見的以時間t為自變量的混合模型,預(yù)測誤差與數(shù)據(jù)波動情況有關(guān),模型預(yù)測效果受數(shù)據(jù)時間性的影響,范圍預(yù)測結(jié)果的精確度和可靠度達(dá)不到理想要求。同時,相鄰兩個時間窗口范圍預(yù)測的周覆蓋比CCR和周可靠比CRR變動較大,模型預(yù)測的穩(wěn)定性較差。為了剔除模型受數(shù)據(jù)時間性的影響,本文摒棄常見的以時間t作為自變量的假設(shè),引入上證指數(shù)每日最高點(diǎn)、每日最低點(diǎn)、每日收盤點(diǎn)、每日成交量(萬手)和每日成交額(億)作為自變量,重新組建混合模型。從實(shí)證分析的結(jié)果來看,該種做法能夠有效排除數(shù)據(jù)時間性對預(yù)測結(jié)果的影響,大幅提高范圍預(yù)測的精確度和可靠度。同時,該種混合模型得到的相鄰兩個時間窗口范圍預(yù)測的周覆蓋比CCR和周可靠比CRR變動較小,模型預(yù)測的穩(wěn)定性較高。
[Abstract]:The arrival of big data has brought a new challenge to data mining. How to mine useful information in mass data becomes a major topic. Can greatly reduce the complexity of information, simplify the process of solving the problem, This paper combines fuzzy information granulation model based on different membership functions with support vector (regression) machine model to form a hybrid model. The regression analysis of Shanghai Stock Exchange index is carried out to find the membership function that makes the best prediction effect of the hybrid model. The empirical analysis of Shanghai Stock Exchange Index shows that the mixed model based on different membership functions has the same prediction results for the median R of fuzzy particles. The range prediction accuracy and reliability of the hybrid model based on asymmetric parabola membership function are the highest. However, the common mixed model with time t as independent variable, the prediction error is related to the fluctuation of data. The prediction effect of the model is affected by the time of data, and the accuracy and reliability of the range prediction results are not up to the ideal requirement. At the same time, the circumferential coverage ratio (CCR) and the cycle reliability of the adjacent two time windows are more variable than that of CRR. The stability of model prediction is poor. In order to eliminate the influence of data timeliness, this paper discards the assumption that time t is the independent variable, and introduces the daily highest point, daily low point and daily closing point of Shanghai stock index. Daily turnover (10 million hands) and daily turnover (100 million) are taken as independent variables to reconstruct the mixed model. From the results of empirical analysis, this method can effectively eliminate the impact of data timeliness on the forecast results. The accuracy and reliability of range prediction are greatly improved. At the same time, the predicted coverage ratio (CCR) and reliability ratio (CCR) of the two adjacent time windows obtained by this hybrid model are smaller than those of CRR, and the stability of the model prediction is higher.
【學(xué)位授予單位】：蘭州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F832.51;F224

【參考文獻(xiàn)】

相關(guān)期刊論文 前4條

1 姜奇平;;大數(shù)據(jù)時代到來[J];互聯(lián)網(wǎng)周刊;2012年02期

2 ;A Granular Computing Model Based on Tolerance relation[J];The Journal of China Universities of Posts and Telecommunications;2005年03期

3 張鈴,張鈸;模糊商空間理論(模糊粒度計算方法)[J];軟件學(xué)報;2003年04期

4 苗奪謙,范世棟;知識的粒度計算及其應(yīng)用[J];系統(tǒng)工程理論與實(shí)踐;2002年01期

相關(guān)博士學(xué)位論文 前1條

1 鮑漪瀾;基于支持向量機(jī)的金融時間序列分析預(yù)測算法研究[D];大連海事大學(xué);2013年

相關(guān)碩士學(xué)位論文 前2條

1 王芳;基于支持向量機(jī)的滬深300指數(shù)回歸預(yù)測[D];山東大學(xué);2015年

2 張豐旺;基于信息�；腟VM在證券時間序列分析中的應(yīng)用[D];昆明理工大學(xué);2014年

，

本文編號：1523051