本文選題：時(shí)間序列　切入點(diǎn)：數(shù)據(jù)挖掘　出處：《暨南大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著計(jì)算機(jī)技術(shù)、人工智能、機(jī)器學(xué)習(xí)以及統(tǒng)計(jì)分析方法的有機(jī)融合和發(fā)展，數(shù)據(jù)挖掘技術(shù)得到了迅猛發(fā)展，且伴隨著大數(shù)據(jù)時(shí)代的到來(lái)，傳統(tǒng)的金融分析方法已經(jīng)逐漸無(wú)法滿足金融數(shù)據(jù)分析的應(yīng)用和要求，采用數(shù)據(jù)挖掘的方法對(duì)金融時(shí)間序列數(shù)據(jù)進(jìn)行分析逐漸成為了金融研究的潮流。 在這一背景下，本文對(duì)金融時(shí)間序列中的股價(jià)時(shí)間序列數(shù)據(jù)進(jìn)行挖掘建模研究，同時(shí)，鑒于目前特質(zhì)波動(dòng)率對(duì)股價(jià)趨勢(shì)的預(yù)測(cè)這方面的研究還相對(duì)較少，因此，本文采用自定義的TBUD方法對(duì)股價(jià)時(shí)間序列進(jìn)行集合劃分，并采用支持向量機(jī)進(jìn)行建模，研究集合的特質(zhì)波動(dòng)率屬性對(duì)趨勢(shì)的預(yù)測(cè)能力。本文的實(shí)證研究發(fā)現(xiàn)，，TBUD方法所劃分的集合之間的特質(zhì)波動(dòng)率屬性差異并不顯著，特質(zhì)波動(dòng)率無(wú)法對(duì)股價(jià)趨勢(shì)做出準(zhǔn)確的預(yù)測(cè)。 本文提出時(shí)間序列上的拐點(diǎn)集合劃分方法，擺脫從回歸方程上來(lái)對(duì)時(shí)間序列進(jìn)行預(yù)測(cè)，而是從數(shù)據(jù)挖掘的角度，研究拐點(diǎn)集合與趨勢(shì)的相關(guān)性，為以后的研究提供一個(gè)新的方向。
[Abstract]:With the integration and development of computer technology, artificial intelligence, machine learning and statistical analysis methods, data mining technology has been rapidly developed, and accompanied by the arrival of big data era. The traditional method of financial analysis has been unable to meet the application and requirement of financial data analysis. Using the method of data mining to analyze the financial time series data has gradually become the trend of financial research. In this context, this paper studies the mining and modeling of stock price time series data in financial time series. At the same time, in view of the fact that there is relatively little research on the prediction of stock price trend by idiosyncratic volatility, therefore, In this paper, we use the custom TBUD method to partition the stock price time series, and use support vector machine to model the stock price time series. In this paper, we find that there is no significant difference in the trait volatility attributes between the set and the TBUD method, and the trait volatility can not accurately predict the stock price trend. In this paper, a method of dividing the inflection point set in time series is proposed to predict the time series from the regression equation, but the correlation between the inflection point set and the trend is studied from the angle of data mining. It provides a new direction for future research.
【學(xué)位授予單位】：暨南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F224;F830.91

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