【摘要】：隨著中國(guó)經(jīng)濟(jì)的迅猛發(fā)展，理財(cái)?shù)母拍钪饾u在大眾心理建立起來(lái)，而股票就是一直受大眾青睞的理財(cái)產(chǎn)品。股票是市場(chǎng)經(jīng)濟(jì)融資的重要手段之一，股市的發(fā)展不僅體現(xiàn)國(guó)家經(jīng)濟(jì)的發(fā)展，更是關(guān)乎千家萬(wàn)戶(hù)的切身利益。大自然的混沌現(xiàn)象無(wú)處不在，由大自然的產(chǎn)物——人所一手操辦的股市必然也是一個(gè)混沌的系統(tǒng)。因此，本文將通過(guò)混沌時(shí)間序列及彈性反饋神經(jīng)網(wǎng)絡(luò)對(duì)股票價(jià)格走勢(shì)進(jìn)行預(yù)測(cè)，具體內(nèi)容安排如下： 首先，介紹混沌動(dòng)力學(xué)以及混沌時(shí)間序列的相關(guān)理論。先介紹混沌的現(xiàn)象、混沌的定義、混沌的基本特性、李雅普諾夫指數(shù)等基礎(chǔ)的混沌學(xué)知識(shí)。緊接著簡(jiǎn)要介紹了混沌時(shí)間序列的知識(shí)，重點(diǎn)包括相空間重構(gòu)技術(shù)、時(shí)間延遲和嵌入維數(shù)的確定以及最大李雅普諾夫指數(shù)預(yù)測(cè)方法。 其次，介紹了神經(jīng)網(wǎng)絡(luò)的基礎(chǔ)知識(shí)，詳細(xì)的介紹了反饋神經(jīng)網(wǎng)絡(luò)的原理，重點(diǎn)介紹了反饋神經(jīng)網(wǎng)絡(luò)的主要算法并比較他們的優(yōu)缺點(diǎn)，最終論證了為何選擇彈性神經(jīng)網(wǎng)絡(luò)算法作為本文的預(yù)測(cè)方法。 最后，利用混沌時(shí)間序列和彈性反饋神經(jīng)網(wǎng)絡(luò)結(jié)合的方法對(duì)某只股票數(shù)據(jù)進(jìn)行預(yù)測(cè)分析，將預(yù)測(cè)結(jié)果與最大李雅普諾夫指數(shù)預(yù)測(cè)結(jié)果及經(jīng)典反饋神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)結(jié)果進(jìn)行比較。 研究表明，結(jié)合混沌時(shí)間序列和彈性反饋算法對(duì)股票進(jìn)行預(yù)測(cè)，無(wú)論在精度還是性能上都取得了更好的效果。
[Abstract]:With the rapid development of Chinese economy, the concept of financial management is gradually established in the popular psychology, and the stock is always favored by the masses. Stock is one of the important means of market economy financing. The development of stock market not only reflects the development of national economy, but also relates to the vital interests of thousands of households. The chaos of nature is everywhere, and the stock market run by man is a chaotic system. Therefore, in this paper, the stock price trend is predicted by chaotic time series and elastic feedback neural network. The main contents are as follows: firstly, the chaotic dynamics and chaotic time series theory are introduced. This paper first introduces the phenomena of chaos, the definition of chaos, the basic characteristics of chaos, and the basic knowledge of chaos such as Lyapunov exponent. Then, the knowledge of chaotic time series is briefly introduced, including the reconstruction of phase space, the determination of time delay and embedding dimension, and the prediction method of maximum Lyapunov exponent. Secondly, the basic knowledge of neural network is introduced, the principle of feedback neural network is introduced in detail, the main algorithms of feedback neural network are introduced, and their advantages and disadvantages are compared. Finally, the paper demonstrates why the elastic neural network algorithm is chosen as the prediction method in this paper. Finally, the method of combining chaotic time series with elastic feedback neural network is used to predict and analyze the stock data. The prediction results are compared with those of the largest Lyapunov exponent and the classical feedback neural network. The results show that the accuracy and performance of stock prediction are improved by using chaotic time series and elastic feedback algorithm.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：F832.51;F224

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