关键词: 离群特征模式, 支持向量机, 马尔可夫毯, 先验知识

Abstract: Due to the stock price fluctuations have stronger mutation and easily influenced by outside factors, cause it’s difficult to predict stock price movements. A stock market volatility forecasting model based on characteristics of outliers pattern（SFSVM） is presented. Firstly, SFSVM algorithm utilizes Markov Blanket algorithm obtaining local network to shield the effects of other node to the target node; Secondly, analyzing the index of the target node to extract characteristic of outliers pattern from the general behavior；then SFSVM algorithm capture outlier features using sliding window, put characteristic of outliers pattern into original SVM model as a prior knowledge, this method can predict peak point and smooth effect of peak point on the predicted results, it also can improve forecasting model robustness. Experimental results, obtained by running on datasets taken from stock plate index, show that this method performs better than neural network algorithm and the standard SVM algorithm on stock trend projections.

Key words: characteristics of outliers model, Support Vector Machines（SVM）, Markov Blanket, prior knowledge