【摘要】：隨著人類進(jìn)入大數(shù)據(jù)時(shí)代,通過數(shù)據(jù)挖掘技術(shù)將時(shí)間序列數(shù)據(jù)庫中隱藏的、有價(jià)值的知識(shí)挖掘出來得到了愈多的關(guān)注,其相關(guān)技術(shù)己被成功地運(yùn)用到各個(gè)領(lǐng)域。時(shí)間序列相似性度量可以衡量時(shí)間序列之間相似程度的方法,其度量結(jié)果可用于分類、聚類、相似性搜索等數(shù)據(jù)挖掘任務(wù)中。時(shí)間序列聚類是時(shí)間序列數(shù)據(jù)挖掘領(lǐng)域中重要的挖掘任務(wù)之一,不同的時(shí)間序列聚類方法,可以挖掘出不同的隱含信息。本文以時(shí)間序列為研究對(duì)象,探討時(shí)間序列的相似性度量方法和聚類方法,促使方法可以充分與靈活地應(yīng)用到時(shí)間序列數(shù)據(jù)挖掘中,然后擷取潛在珍貴的信息和知識(shí)。本文的主要研究內(nèi)容如下:(1)以數(shù)值分布特性和趨勢波動(dòng)特征為出發(fā)點(diǎn),提出基于數(shù)值符號(hào)和形態(tài)特征的相似性度量方法。新方法能夠充分反映時(shí)間序列數(shù)值分布和形態(tài)特征,有效地提高了時(shí)間序列相似性的度量效果。(2)針對(duì)傳統(tǒng)聚類方法通常需要確定具體聚類數(shù)目,及未能充分反映時(shí)間序列整體空間結(jié)構(gòu)和相互影響關(guān)系的問題,提出一種基于中心度的標(biāo)簽傳播時(shí)間序列聚類方法。該方法無需指定具體聚類數(shù)目即可實(shí)現(xiàn)自動(dòng)聚類,并且根據(jù)不同參數(shù)構(gòu)建不同的網(wǎng)絡(luò)空間結(jié)構(gòu),聚類數(shù)目能夠?qū)Υ诉M(jìn)行相應(yīng)地調(diào)整,提高其在時(shí)間序列聚類的性能。(3)動(dòng)態(tài)時(shí)間彎曲和時(shí)間序列聚類在金融領(lǐng)域的應(yīng)用。一方面,以動(dòng)態(tài)時(shí)間彎曲和經(jīng)典時(shí)間序列聚類方法為基礎(chǔ),在金融領(lǐng)域進(jìn)行進(jìn)一步探索。針對(duì)股票聯(lián)動(dòng)性的研究,挖掘股票的隱含信息,對(duì)監(jiān)管部門和投資者決策起著一定幫助作用。另一方面,以滬深300指數(shù)為標(biāo)的指數(shù),利用新的相似性度量方法和聚類方法對(duì)現(xiàn)貨股票進(jìn)行聚類分析,選定追蹤成分股,并建立優(yōu)化模型來獲得成分股在投資組合中的優(yōu)化權(quán)重,并使得新方法確定的成分股更能準(zhǔn)確地模擬標(biāo)的指數(shù),且能夠滿足不同投資喜好的投資者投資要求。研究內(nèi)容通過數(shù)值實(shí)驗(yàn)分析,并且通過比較研究領(lǐng)域的相關(guān)方法,檢驗(yàn)了新方法的性能,進(jìn)一步完善時(shí)間序列相似性度量和聚類的研究,同時(shí)在一定程度上擴(kuò)展了時(shí)間序列數(shù)據(jù)挖掘相關(guān)理論和提升了方法在金融時(shí)間序列數(shù)據(jù)領(lǐng)域中的應(yīng)用性能。
[Abstract]:With the entry of big data era, the more attention has been paid to mining the valuable knowledge hidden in time series database through data mining technology, the more attention has been paid to it, and its related technology has been successfully applied to various fields. The similarity measurement of time series can be used to measure the degree of similarity among time series, and the results can be used in data mining tasks such as classification, clustering, similarity search and so on. Time series clustering is one of the important mining tasks in the field of time series data mining. Different time series clustering methods can mine different hidden information. Taking time series as the research object, this paper discusses the similarity measurement method and clustering method of time series, so that the method can be fully and flexibly applied to time series data mining, and then extract potentially precious information and knowledge. The main contents of this paper are as follows: (1) the similarity measurement method based on numerical symbols and morphological features is proposed based on numerical distribution characteristics and trend fluctuation characteristics as the starting point. The new method can fully reflect the numerical distribution and morphological characteristics of time series and effectively improve the effect of measuring the similarity of time series. (2) in view of the traditional clustering methods, it is usually necessary to determine the number of specific clusters. In this paper, a clustering method of label propagation time series based on centrality is proposed, which fails to fully reflect the global spatial structure and interaction relationship of time series. This method can realize automatic clustering without specifying the number of clusters, and construct different spatial structure of network according to different parameters, which can be adjusted accordingly. Improve its performance in time series clustering. (3) the application of dynamic time bending and time series clustering in financial field. On the one hand, based on dynamic time bending and classical time series clustering methods, further exploration is carried out in the field of finance. In view of the research of stock association, mining the implicit information of stock plays a certain role in the decision-making of regulators and investors. On the other hand, taking the Shanghai-Shenzhen 300 index as the target index, we use the new similarity measure method and the clustering method to cluster the spot stock and select the tracking component stock. The optimization model is established to obtain the optimal weight of the component stocks in the portfolio, and make the component stocks determined by the new method can more accurately simulate the underlying index, and can meet the investment requirements of investors with different investment preferences. The research contents are analyzed by numerical experiments, and the performance of the new method is tested by comparing the related methods in the field of research, and the research on similarity measurement and clustering of time series is further improved. At the same time, the theory of time series data mining is extended to a certain extent and the application performance of the method in the field of financial time series data is improved.
【學(xué)位授予單位】：華僑大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP311.13

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