結合整體經驗模態分解、基因演算法與極速學習機於財務時間序列預測之研究

Financial Time Series Forecasting Using Ensemble Empirical Decomposition Mode, Genetic Algorithm and Extreme Learning Machine

10.6841/NTUT.2011.00054

林政弘

財務時間序列預測 ； 極速學習機 ； 經驗模態分解 ； 基因演算法 ； Stock Price Prediction ； Extreme Learning Machine ； Ensemble Empirical Mode Decomposition ； Genetic Algorithm

臺北科技大學商業自動化與管理研究所學位論文

2011年

碩士

林鳳儀

繁體中文

準確地預測股票價格，已為投資決策重要的問題，由於金融時間序列具有非線性及非穩態之特性，難以運用統計模型進行預測。而類神經網路，使用上較無需嚴格的理論假設，因此被大量應用於財務預測。在類神經網路之演算法中，極速學習機(Extreme Learning Machine)克服了傳統倒傳遞(Back Propagation)演算法所面臨的缺陷，而倍受矚目。 本研究以台灣加權股價指數、上海綜合股價指數、香港恆生指數之2001年至2010年收盤價為研究對象，提出了四種預測模型。其中極速學習機用於建立預測模型；整體經驗模態分解(Ensemble Empirical Mode Decomposition)用於把股價分解成較易於預測之本質模態函數；而基因演算法(Genetic Algorithm)則試圖找出模型最佳參數。另外為驗證所提模型優於傳統統計模型，所提四種模型也與ARIMA模型比較。 研究結果指出，結合極速學習機、基因演算法、整體經驗模態分解之模型有最好的預測成效；而所提四種模型之預測成效皆優於ARIMA模型，可見所提模型之優越。

Financial time series are inherently nonlinear and non-stationary, it is therefore difficult using statistical models to forecast. ANN(Artificial Neural Networks)does not require strict theoretical assumptions, so it has been widely applied for financial prediction. On ANN learning algorithms, the ELM(Extreme Learning Machine) overcomes the drawback of traditional Back-propagation. This study takes the closing price of Taiwan Capitalization Weighted Stock Index, Shanghai Stock Exchange Composite Index and Hong Kong Hang Seng Index as research subjects during the period of 2001 to 2010. We propose a hybrid forecasting model based on EEMD(Ensemble Empirical Mode Decomposition), GA (Genetic Algorithm) and ELM. Firstly, by using EEMD to decompose stock price into several IMF(Intrinsic Mode Functions) and each IMF component is modeled by individual EELM respectively. Then, we find the optimal parameters with GA. In order to examine the proposed models are better than traditional statistical models, these four models also compare with the ARIMA model. The study concluded the model combined with ELM, GA, EEMD, which has the best prediction performance. The performance of proposed four models is better than ARIMA models, showing the excellence of proposed models.

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