本文關(guān)鍵詞：基于時間序列相似性的股價趨勢預測研究　出處：《重慶交通大學》2014年碩士論文　論文類型：學位論文

  更多相關(guān)文章： 時間序列 類比合成 相似度 趨勢預測

【摘要】：隨著計算機技術(shù)應用范圍逐步擴大，時間序列數(shù)據(jù)頻繁出現(xiàn)于交通、商業(yè)、科學、金融等各個領域，時間序列數(shù)據(jù)分析處理技術(shù)被越來越多人重視。傳統(tǒng)時間序列分析預測方法往往將時間序列數(shù)據(jù)匹配到某些數(shù)學模型中，然后再對其整體進行分析和預測，但是現(xiàn)實中許多數(shù)據(jù)不能夠滿足模型參數(shù)要求。針對于此，基于時間序列相似性的類比合成預測方法以其非參數(shù)回歸特性成為該領域研究焦點。 本文在對時間序列相似性度量方法和類比合成方法的研究基礎上，提出了一種不等長時間序列相似性度量方法，并設計出了適用性較強的時間序列趨勢預測方案，以真實股票價格數(shù)據(jù)為基礎進行了實證分析，主要工作及創(chuàng)新點如下： 第一，對時間序列相似性度量方法做深入研究，并對時間序列中經(jīng)常出現(xiàn)的振幅平移、振幅伸縮、線性漂移和時間軸伸縮等形變做詳細討論，認為優(yōu)秀的時間序列相似性度量方法應該對上述形變不敏感。 第二，對時間序列預測的類比合成方法進行深入研究，并對非參數(shù)回歸模型做相關(guān)討論，類比合成方法作為一種典型的非參數(shù)回歸方法，，具有良好的應用前景。 第三，提出改進型余弦公式的不等長時間序列相似性度量（RCBS_UL）算法，該算法在原始余弦公式的基礎上通過對序列進行等長化處理、歸一化處理，最終實現(xiàn)不等長時間序列的相似性度量，該方法對振幅平移、振幅伸縮、線性漂移和時間軸伸縮不敏感。 第四，將時間序列預測的類比合成方法和RCBS_UL算法相結(jié)合，設計出一種時間序列趨勢預測方案，并以真實股票價格指數(shù)為基礎實驗數(shù)據(jù)，對股票價格走勢進行預測。實驗結(jié)果表明，該方案能夠準確預測股價走勢方向，但對于未來值的確切預測還不能令人滿意，需要做進一步的研究和改進。 第五，結(jié)合RCBS_UL算法和相似搜索技術(shù)，設計出一種決策支持方案，該方案并沒有對股票價格走勢做預測計算，而是通過搜索并提供經(jīng)典圖形給使用者來支持其預測判斷，從而幫助使用者減輕負擔。
[Abstract]:With the gradual expansion of the application of computer technology, time series data frequently appear in traffic, commerce, science, finance and other fields. Time series data analysis and processing technology has been paid more and more attention. Traditional time series analysis and prediction methods often match time series data to some mathematical models and then analyze and predict the whole time series data. However, many data can not meet the requirements of model parameters in reality. In view of this, analogical composite prediction method based on similarity of time series has become the focus of research in this field because of its non-parametric regression characteristics. On the basis of the research of similarity measurement and analogical composition of time series, an unequal time series similarity measurement method is proposed in this paper. And designed a more applicable time series trend prediction scheme, based on the real stock price data for empirical analysis, the main work and innovation as follows: First, the similarity measurement method of time series is deeply studied, and the deformation such as amplitude translation, amplitude stretching, linear drift and time axis stretching are discussed in detail. It is considered that the excellent time series similarity measurement method should be insensitive to the above deformation. Secondly, the analogue synthesis method of time series prediction is studied in depth, and the non-parametric regression model is discussed. As a typical non-parametric regression method, analogue synthesis method is used as a typical non-parametric regression method. It has good application prospect. Thirdly, the RCBSULL algorithm of the improved cosine formula is proposed, which is based on the original cosine formula and is processed by equal-length processing on the basis of the original cosine formula. The method is not sensitive to amplitude translation, amplitude stretching, linear drift and time axis scaling. In 4th, combining the analog synthesis method of time series prediction with RCBS_UL algorithm, we designed a time series trend prediction scheme, and based on the real stock price index as the experimental data. The experimental results show that the scheme can accurately predict the direction of stock price trend, but the exact prediction of the future value is not satisfactory, which needs further study and improvement. 5th, combined with RCBS_UL algorithm and similar search technology, a decision support scheme is designed, which does not predict the stock price trend. It helps users to lighten their burden by searching and providing classical graphics to users to support their prediction judgment.
【學位授予單位】：重慶交通大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F830.91;F224

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