本文選題：時間序列　切入點：技術(shù)分析　出處：《昆明理工大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：復(fù)雜的時間序列往往蘊含了很多潛在的重要信息和事物規(guī)律,我們對這類重要的復(fù)雜數(shù)據(jù)對象進(jìn)行詳盡的分析后,便有可能揭示事物運動、變化和發(fā)展的內(nèi)在規(guī)律。金融時向序列是金融資產(chǎn)收益序列的重要表現(xiàn)形式,例如股票、基金、外匯、金融衍生品等收益率的分時線、日線等,也是經(jīng)濟(jì)與金融領(lǐng)域中最重要的數(shù)據(jù),因此對這類數(shù)據(jù)分析和預(yù)測在金融投資預(yù)測、決策和風(fēng)險管理等方面具有重要意義。 目前,各種市場分析技術(shù)應(yīng)用于解釋股票市場和預(yù)測市場的未來趨勢,這些技術(shù)不僅需要一定程度在金融和經(jīng)濟(jì)學(xué)的專業(yè)知識,還要收集大量關(guān)于市場的數(shù)據(jù),而且需要很多的計算,這對中小投資者都需要花費太多的精力。人類求解問題具有魯棒性,處理問題的能力隨著經(jīng)驗的增長而不斷地增強,復(fù)用以前經(jīng)驗的方法是人類專家的一種基本而重要的解決問題方法,由于人工智能中的CBR推理技術(shù)同人類推理十分相似,所以本文提出一種基于技術(shù)分析和CBR的證券時間序列預(yù)測模型。 案例推理(case based reasoning, CBR)主要包含案例檢索、案例重用、案例修正和案例保存四個過程。本文首先結(jié)合給定個股的證券時間序列的技術(shù)形態(tài)特征,利用基于最大最小點實現(xiàn)了典型技術(shù)形態(tài)的模式識別,及識別形態(tài)模式的起止時間、成交量、MA、OBV、RSI等指標(biāo)屬性值的信息,以連續(xù)3個形態(tài)模式作為一個完整的案例,構(gòu)建案例庫并表示案例。然后,利用案例檢索的相似匹配算法—NN算法,檢索出與目標(biāo)案例相似的已經(jīng)存在于案例庫中的源案例,進(jìn)行相似度與入庫閾值的比較,最終實現(xiàn)對證券時間序列未來走勢的預(yù)測,并驗證了該模型在理論和實際應(yīng)用中的準(zhǔn)確性及有效性。
[Abstract]:Complex time series often contain a lot of potentially important information and rules of things. After we analyze these important and complex data objects in detail, it is possible to reveal the movement of things. The inherent law of change and development. The financial time-series is an important manifestation of the series of returns on financial assets, such as the time-sharing and diurnal lines of returns such as stocks, funds, foreign exchange, financial derivatives, etc. It is also the most important data in the field of economy and finance. Therefore, the analysis and prediction of this kind of data is of great significance in financial investment prediction, decision making and risk management. At present, a variety of market analytical techniques are used to explain stock markets and predict future trends in markets that require not only a degree of expertise in finance and economics, but also the collection of a large amount of data on markets, And it takes a lot of computing, and it takes a lot of effort for small and medium investors. Human solutions are robust, and the ability to deal with them increases as experience grows. The method of reusing previous experience is a basic and important problem solving method for human experts, because the CBR reasoning technology in artificial intelligence is very similar to human reasoning. So this paper presents a forecasting model of securities time series based on technical analysis and CBR. Case-based reasoning (CBR) mainly includes four processes: case retrieval, case reuse, case correction and case preservation. The pattern recognition based on the maximum and the minimum points is used to realize the pattern recognition of typical technical forms, and the information of the starting and ending time of the recognition of the morphological pattern, the value of the RSI index such as MAOBV / RSI, etc., are used as a complete case, and the continuous three morphological patterns are taken as a complete case. The case base is constructed and the case is represented. Then, the similarity matching algorithm -NN algorithm is used to retrieve the source case which is similar to the target case in the case database, and the similarity is compared with the threshold value. Finally, the prediction of the future trend of securities time series is realized, and the accuracy and validity of the model in theoretical and practical applications are verified.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP18;F830.91

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