本文選題：股票 + 時(shí)間序列��； 參考：《浙江工業(yè)大學(xué)》2012年碩士論文

【摘要】：股票的股價(jià)序列是一個(gè)典型的時(shí)間序列，利用時(shí)間序列技術(shù)對(duì)股價(jià)序列進(jìn)行研究分析有一定的理論意義和實(shí)用價(jià)值。通過(guò)研究時(shí)間序列相關(guān)理論和方法，將其應(yīng)用于股價(jià)序列，可發(fā)現(xiàn)股價(jià)時(shí)間序列的內(nèi)在變化規(guī)律，從而對(duì)其進(jìn)行趨勢(shì)預(yù)測(cè)分析。研究利用股價(jià)時(shí)間序列建模技術(shù)，并為投資者提供一個(gè)能自動(dòng)化、智能化分析股市信息的工具，是本文的研究目標(biāo)。本文的主要研究?jī)?nèi)容包含了以下幾個(gè)方面： (1)設(shè)計(jì)和實(shí)現(xiàn)了一個(gè)股票信息處理分析系統(tǒng)，能實(shí)現(xiàn)用戶管理、技術(shù)分析、條件選股等常規(guī)股票分析功能。 (2)提出了一種改進(jìn)的適用于股票股價(jià)序列的擬合算法，，該算法的思想是采用斜率法和三角中線法相結(jié)合的辦法來(lái)尋找股票的股價(jià)關(guān)鍵趨勢(shì)點(diǎn)作為分段點(diǎn)，進(jìn)而對(duì)序列進(jìn)行分段線性擬合，最后在行情處理系統(tǒng)中對(duì)算法進(jìn)行實(shí)現(xiàn)。在實(shí)證研究中，通過(guò)與幾種常見(jiàn)擬合算法的比較發(fā)現(xiàn)，該改進(jìn)的算法在數(shù)據(jù)壓縮和對(duì)股票趨勢(shì)的提取這兩個(gè)方面具有更好的效果；最后將此改進(jìn)的算法融入到股票信息處理分析系統(tǒng)中，具有操作方便，能迅速獲取股票關(guān)鍵信息等優(yōu)點(diǎn)。 (3)針對(duì)如何選擇輸入特征向量能使支持向量機(jī)的預(yù)測(cè)效果更加精準(zhǔn)這一問(wèn)題，本文提出了兩種改進(jìn)的算法：一是運(yùn)用關(guān)鍵點(diǎn)查找算法來(lái)對(duì)原始的股票信息進(jìn)行特征選擇，選擇股價(jià)序列中能代表股票整體走勢(shì)的序列作為SVM的輸入特征向量；二是通過(guò)決策樹的信息增益法來(lái)判定股票輸入特征的重要程度，再根據(jù)信息增益值來(lái)對(duì)特征進(jìn)行加權(quán)計(jì)算后作為SVM的輸入特征向量；最后將此兩種改進(jìn)的算法融入到股票信息處理分析系統(tǒng)中，能在很大程度上提高了預(yù)測(cè)結(jié)果的精度。 總之，本文在實(shí)現(xiàn)了股票分析的基本功能之上，又結(jié)合上述研究的時(shí)間序列算法，側(cè)重實(shí)現(xiàn)了對(duì)于股價(jià)序列的基于關(guān)鍵點(diǎn)的SVM預(yù)測(cè)功能和基于決策樹加權(quán)特征選擇的SVM預(yù)測(cè)功能，使得系統(tǒng)具有其他股票分析系統(tǒng)所沒(méi)有的功能。
[Abstract]:The stock price sequence is a typical time series. It has some theoretical and practical value to study the stock price sequence by using time series technology. By studying the theory and method of time series related to the stock price sequence, we can find the internal change law of the time sequence of the stock price, so as to carry on the trend of the trend. The research aim of this paper is to use the time series modeling technology of stock price and provide an automatic and intelligent tool for investors to analyze the stock market information. The main research contents of this paper include the following aspects:
(1) designed and implemented a stock information processing and analysis system, which can realize user stock management, technical analysis, conditional stock selection and other conventional stock analysis functions.
(2) an improved fitting algorithm suitable for stock stock price sequence is proposed. The idea of this algorithm is to use the method of slope and triangular midline to find the key point of stock price as a piecewise point, and then piecewise linear fitting to the sequence. Finally, the algorithm is realized in the market processing system. In the study, by comparing with several common fitting algorithms, it is found that the improved algorithm has two advantages in data compression and the extraction of stock trend. Finally, the improved algorithm is integrated into the stock information processing and analysis system, which has the advantages of convenient operation and quick acquisition of the key information of stock.
(3) in order to select the input feature vector to make the support vector machine more accurate, this paper proposes two improved algorithms: first, using the key point search algorithm to select the original stock information, choose the sequence of the overall trend of the stock in the stock price sequence as the input feature of the SVM The two is to determine the importance of the input characteristics of the stock by the information gain method of the decision tree, and then weigh the features according to the gain value of the information as the input feature vector of the SVM. Finally, the two improved algorithms are integrated into the stock information processing and analysis system, and the prediction results can be greatly improved. Precision.
In conclusion, this paper has realized the basic function of stock analysis, and combined with the time series algorithm of the above research, it lays particular emphasis on realizing the SVM forecasting function based on the key point of the stock price sequence and the SVM prediction function based on the decision tree weighted feature selection, which makes the system have its function that his stock analysis system has not.

【學(xué)位授予單位】：浙江工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TP311.52;F830.91

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本文編號(hào)：1820077