CAJ | 학술논문

由于股票价格波动具有较强的突变性且易受外界因素影响，导致股票价格走势难以预测。提出基于离群特征模式的股市波动预测模型（SFSVM）。该算法首先利用马尔可夫毯选取目标结点的局部网络结构，以屏蔽其他结点对目标结点的影响；对目标结点的指标进行分析，提取异于一般行为的离群特征模式；利用滑动窗口捕捉离群特征，将离群特征模式作为先验知识加入原SVM模型，预测尖峰点并平滑尖峰点对于预测结果的影响，提高预测模型的稳健性。在股票板块数据上进行实验结果证明，SFSVM算法相对于神经网络和标准的SVM算法，在股票的走势预测方面有更好的预测效果。
유우고표개격파동구유교강적돌변성차역수외계인소영향，도치고표개격주세난이예측。제출기우리군특정모식적고시파동예측모형（SFSVM）。해산법수선이용마이가부담선취목표결점적국부망락결구，이병폐기타결점대목표결점적영향；대목표결점적지표진행분석，제취이우일반행위적리군특정모식；이용활동창구포착리군특정，장리군특정모식작위선험지식가입원SVM모형，예측첨봉점병평활첨봉점대우예측결과적영향，제고예측모형적은건성。재고표판괴수거상진행실험결과증명，SFSVM산법상대우신경망락화표준적SVM산법，재고표적주세예측방면유경호적예측효과。
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.