發(fā)布時(shí)間：2018-08-18 13:02

【摘要】：針對(duì)金融時(shí)間序列的復(fù)雜性，本文將經(jīng)驗(yàn)?zāi)B(tài)分解（EMD）引入金融時(shí)間序列預(yù)測(cè)框架中進(jìn)行研究。EMD多應(yīng)用于通信、氣象領(lǐng)域的數(shù)據(jù)處理，而應(yīng)用于金融領(lǐng)域則不多，但是它具有明顯的優(yōu)點(diǎn)：能根據(jù)數(shù)據(jù)本身的時(shí)間尺度特征準(zhǔn)確反映原時(shí)間序列的物理特性而不造成信號(hào)損失，而且無(wú)需預(yù)先設(shè)定任何基函數(shù)，這與小波分析、傅里葉變換等方法有本質(zhì)的區(qū)別。但EMD存在模態(tài)混疊問(wèn)題，因此需要對(duì)EMD方法進(jìn)行改進(jìn)和優(yōu)化以提高預(yù)測(cè)的有效性。 本文首先構(gòu)建了總體經(jīng)驗(yàn)?zāi)B(tài)分解（EEMD）模型，基于獲取的原始數(shù)據(jù)，模擬產(chǎn)生多條路徑的修正數(shù)據(jù)，每一次修正的數(shù)據(jù)中加入不同的白噪聲以抵消原始數(shù)據(jù)中的噪聲，對(duì)每個(gè)修正的序列進(jìn)行EMD分解，而后取多次分解的平均值作為最后的分解序列，從而升了序列的信噪比，解決模態(tài)混疊問(wèn)題。之后，將支持向量回歸（SVR）模型引入到金融時(shí)間序列分析，同時(shí)，采用多種群遺傳算法（MPGA）進(jìn)行SVR的參數(shù)尋優(yōu)。MPGA引入多個(gè)種群同時(shí)進(jìn)行遺傳進(jìn)化搜索，對(duì)不同的種群設(shè)置相應(yīng)的控制參數(shù)，并在不同種群之間依靠移民算子完成信息交互，最終在多個(gè)種群協(xié)同進(jìn)化下得到最優(yōu)解，，可以有效地防止早熟，大大提高收斂速度。 最后，基于前文構(gòu)建的EMD與SVR的改進(jìn)模型，在趨勢(shì)交易中進(jìn)行應(yīng)用。構(gòu)建EEMD-MPGA-SVR預(yù)測(cè)模型。應(yīng)用的結(jié)果表明：其一，對(duì)比不同參數(shù)尋優(yōu)的SVR模型發(fā)現(xiàn)，不論是否進(jìn)行EEMD分解，與網(wǎng)格搜索法SVR、標(biāo)準(zhǔn)遺傳算法SVR相比，多種群遺傳算法SVR的參數(shù)估計(jì)及其預(yù)測(cè)效果都是最好的。其二，對(duì)比進(jìn)行EEMD分解前后的預(yù)測(cè)效果發(fā)現(xiàn)，基于EEMD分解的SVR預(yù)測(cè)效果明顯優(yōu)于直接采用原始序列的SVR預(yù)測(cè)（偏差較�。夷茌^快地捕捉市場(chǎng)信息，由此所觸發(fā)交易的累計(jì)收益率也更高，平均累計(jì)收益率也更高，從而收益的穩(wěn)定性更強(qiáng)。
[Abstract]:In view of the complexity of financial time series, empirical mode decomposition (EMD) is introduced into the framework of financial time series prediction. EMD is mostly used in communication, meteorological data processing, but not in financial field. But it has obvious advantages: it can accurately reflect the physical characteristics of the original time series according to the time scale characteristics of the data without causing signal loss, and it does not need to set any basis function in advance, which is similar to wavelet analysis. Fourier transform and other methods have essential differences. However, EMD has the problem of modal aliasing, so it is necessary to improve and optimize the EMD method to improve the effectiveness of prediction. In this paper, a global empirical mode decomposition (EEMD) model is constructed. Based on the original data obtained, the modified data of multiple paths are simulated, and different white noises are added to each modified data to offset the noise in the original data. Each modified sequence is decomposed by EMD, and then the average value of multiple decomposition is taken as the final decomposition sequence, which increases the SNR of the sequence and solves the problem of mode aliasing. Then, the support vector regression (SVR) model is introduced into the financial time series analysis. At the same time, the multi-population genetic algorithm (MPGA) is used to optimize the parameters of SVR. By setting corresponding control parameters for different populations and interacting information among different populations by means of immigration operators, the optimal solution can be obtained under the coevolution of multiple populations, which can effectively prevent premature maturing and greatly improve the convergence rate. Finally, based on the improved model of EMD and SVR, it is applied in trend trading. EEMD-MPGA-SVR prediction model is constructed. The application results show that: firstly, compared with the SVR model with different parameters, it is found that, whether or not EEMD is decomposed, it is compared with the grid search method and the standard genetic algorithm (SVR). Multi-population genetic algorithm (SVR) is the best in parameter estimation and prediction. Secondly, comparing the prediction results before and after EEMD decomposition, it is found that the prediction effect of SVR based on EEMD decomposition is obviously better than that of SVR prediction using the original sequence directly (the deviation is small), and it can capture market information quickly. The cumulative yield and the average cumulative yield are higher, so the return is more stable.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F830.91;F224;TP18

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