本文選題：嵌入向量　切入點：金融時間序列　出處：《吉林大學》2017年碩士論文　論文類型：學位論文

【摘要】：金融是現(xiàn)代經(jīng)濟的核心,也是調(diào)控宏觀經(jīng)濟的重要杠桿,它對于人類社會具有非常重要的作用。金融具有兩面性,帶給我們利益的同時也帶來一系列消極影響,如通貨膨脹、金融泡沫、金融危機等。為使金融帶來的負面影響降至最低,對金融的相關分析則至關重要,金融的分析可以通過對其建立時間序列模型來實現(xiàn)。所謂金融時間序列是指金融相關數(shù)據(jù)按時間順序排列而成的數(shù)列,對金融時間序列的分析能夠直接的反應出金融的活動規(guī)律。金融時間序列中包括股票數(shù)據(jù)按既定時間順序的排列,對股票數(shù)據(jù)的走勢預測分析可側(cè)面反映出金融市場的活動趨勢。在金融時間序列中,不同時刻的數(shù)據(jù)之間必然存在著一定的關系,但金融市場的影響因素眾多而且復雜,導致這種關系很難用清晰的數(shù)學模型刻畫出來,這一點對于金融時間序列的分析和預測有著重要的影響。在自然語言處理領域,已有研究通過所謂的嵌入向量表示方法將文本的上下文在語法語義上的連貫關系刻畫出來,那么同樣的前后具有關聯(lián)的金融時間序列,是否可以使用嵌入向量思想將這種關聯(lián)表現(xiàn)出來,進一步實現(xiàn)金融時間序列的預測,正是本文所要研究的主要內(nèi)容。論文的主要工作可以歸納為兩方面:首先通過引入嵌入向量思想提出了金融日向量和金融周向量兩種金融時間序列的表示方法,以表示這些向量在時間序列中的“上下文”相關性;之后采用提出的金融嵌入向量表示模型,對金融時間序列進行了預測。傳統(tǒng)嵌入向量是指詞嵌入向量和句子嵌入向量,在自然語言處理領域中的應用主要是對文本進行分析。文本中所包含的詞的個數(shù)是有限的,而金融時間序列一般是連續(xù)的,因此要將嵌入向量的思想應用于金融時間序列分析中,首先需要采用離散化方法將金融數(shù)據(jù)映射到一個有限的集合,之后便可將嵌入向量應用于金融時間序列分析中。與“詞嵌入向量”相對應,我們可以得到所謂“金融日向量”,即將某一個離散化之后的股市數(shù)據(jù)映射為一個實數(shù)向量,其對應的是單個交易日的信息。與“詞嵌入向量”類似,得到的“金融日向量”之間能夠反映出它們在金融時間序列中的連貫關系。類似的,與“句子嵌入向量”相對應,我們得到了所謂“金融周向量”,將周一到周五連續(xù)5天交易日的股市數(shù)據(jù)映射為一個實數(shù)向量,對應的是股市一周的信息。為驗證嵌入向量在金融時間序列應用的可行性,本文將所提出的方法應用于標準普爾500指數(shù)數(shù)據(jù)集。選取5種相關的股市參數(shù)作為原始數(shù)據(jù),即開盤價、最高價、最低價、收盤價、交易量5個參數(shù)進行分析。首先對數(shù)據(jù)集進行離散化,并使用本文提出的方法進行訓練得到金融嵌入向量:“金融日向量”和“金融周向量”,之后選用徑向基函數(shù)(Radial Basis Functions,RBF)神經(jīng)網(wǎng)絡作為預測模型,利用得到的金融嵌入向量,分別對日收盤價和周收盤價進行預測分析。實驗的結果表明,金融日向量和金融周向量能夠?qū)崿F(xiàn)對收盤價的預測,與使用原始股市數(shù)據(jù)相比取得了更好的效果。嵌入向量的思想為金融時間序列分析提供了一種新的思路。
[Abstract]:Finance is the core of modern economy, is an important lever of macroeconomic regulation, it plays a very important role in human society. The finance has two sides, bring us interests at the same time also brought a series of negative effects, such as inflation, financial bubble, financial crisis and other negative effects. As the financial brought to a minimum, correlation analysis it is of vital importance to the financial, financial analysis through the establishment of time series model to achieve the so-called financial time series refers to the financial data and arranged in chronological order of the series, on the analysis of financial time series can directly reflect the law of the financial activities. Including stock data according to the established time sequence arrangement in financial time series, the data on the stock trend prediction analysis can reflect the trend of financial market activities. In the financial time series, not the same time The data must have a certain relationship, but the influence factors of financial market are numerous and complex, the relationship is difficult to describe with mathematical model, which has an important impact point for financial time series analysis and prediction. In the field of Natural Language Processing, has been studied by embedding the so-called vector representation will the text in the context of grammatical and semantic coherence relations describe, financial time series, then the same and have related, whether you can use the embedded vector thought this correlation performance, further realize the forecasting of financial time series, is the main content of this paper is to study. The main work of this paper can be divided into two aspects: firstly, by introducing the idea of embedded vector representation on the financial and financial week two vector vector financial time series, on the table These vectors in the time series of the "context" correlation; after using the proposed financial embedding vector representation model of financial time series are forecasted. The traditional embedded vector refers to words and sentences embedded embedded vector vector, in Natural Language Processing in the field should be used mainly for text analysis. The number contained in the document the word is limited, and the financial time series is continuous, so it will be applied to the ideological embedded vector financial time series analysis, first of all need to adopt the discretization method of the financial data is mapped to a finite set, then the embedded vector is applied to the analysis of financial time series corresponding to. And "the word embedded vector", we can get the so-called "financial daily vector" and is mapping the stock market data after one of the discretization is a real vector, which is to be A single trading day. Similar to "word embedded vector" and the "financial daily vector" can reflect between them in financial time series in the coherent relationship. Similar to the corresponding "sentence embedded vector", we get the so-called "financial week, Monday to Friday will be the vector mapping of stock market data 5 consecutive days of trading days as a real vector, corresponding to the stock market a week. To verify the feasibility of embedded in the vector financial time series application, the proposed method is applied to the S & P 500 index data sets. Select 5 kinds of stock market related parameters as the original data, namely the opening price, the most high price, lowest price, closing price, trading volume of 5 parameters. The first set of data discretization, and embedded vector financial training using the method proposed in this paper:" financial daily vector "and" financial week Vector, after using radial basis function (Radial Basis Functions RBF) neural network as the predictive model, the use of financial embedding vectors are obtained, respectively, to the closing price and the closing price of the week were predicted and analyzed. The experimental results show that the financial and financial week on vector vector can be achieved on the prediction of the closing price, compared with the use of the original stock data achieved better results. Provide a new idea for the idea of embedding vector financial time series analysis.

【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F831.51;O211.61

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本文編號：1594344