【摘要】：針對金融時間序列的復雜性，本文將經(jīng)驗模態(tài)分解（EMD）引入金融時間序列預測框架中進行研究。EMD多應用于通信、氣象領域的數(shù)據(jù)處理，而應用于金融領域則不多，但是它具有明顯的優(yōu)點：能根據(jù)數(shù)據(jù)本身的時間尺度特征準確反映原時間序列的物理特性而不造成信號損失，而且無需預先設定任何基函數(shù)，這與小波分析、傅里葉變換等方法有本質的區(qū)別。但EMD存在模態(tài)混疊問題，因此需要對EMD方法進行改進和優(yōu)化以提高預測的有效性。 本文首先構建了總體經(jīng)驗模態(tài)分解（EEMD）模型，基于獲取的原始數(shù)據(jù)，模擬產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，對每個修正的序列進行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)混疊問題。之后，將支持向量回歸（SVR）模型引入到金融時間序列分析，同時，采用多種群遺傳算法（MPGA）進行SVR的參數(shù)尋優(yōu)。MPGA引入多個種群同時進行遺傳進化搜索，對不同的種群設置相應的控制參數(shù)，并在不同種群之間依靠移民算子完成信息交互，最終在多個種群協(xié)同進化下得到最優(yōu)解，，可以有效地防止早熟，大大提高收斂速度。 最后，基于前文構建的EMD與SVR的改進模型，在趨勢交易中進行應用。構建EEMD-MPGA-SVR預測模型。應用的結果表明：其一，對比不同參數(shù)尋優(yōu)的SVR模型發(fā)現(xiàn)，不論是否進行EEMD分解，與網(wǎng)格搜索法SVR、標準遺傳算法SVR相比，多種群遺傳算法SVR的參數(shù)估計及其預測效果都是最好的。其二，對比進行EEMD分解前后的預測效果發(fā)現(xiàn)，基于EEMD分解的SVR預測效果明顯優(yōu)于直接采用原始序列的SVR預測（偏差較�。�，而且能較快地捕捉市場信息，由此所觸發(fā)交易的累計收益率也更高，平均累計收益率也更高，從而收益的穩(wěn)定性更強。
[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.
【學位授予單位】：華南理工大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F830.91;F224;TP18

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