CAJ | 학술논문

首先应用模糊聚类方法将数据分类，以相邻两个聚类中心的中点作为子区间的分界点来划分论域，并以此将时间序列模糊化为模糊时间序列；其次根据证券市场主要量价指标建立了具有多个前件的高阶模糊关系；最后将该模型用于上证股票综合指数和深证股票成分指数的多步预测和涨跌趋势预测。与典型模糊时间序列模型比较，涨跌趋势预测准确率有较大提高，多步预测结果表明模型具有较好的泛化能力。
수선응용모호취류방법장수거분류，이상린량개취류중심적중점작위자구간적분계점래화분론역，병이차장시간서렬모호화위모호시간서렬；기차근거증권시장주요량개지표건립료구유다개전건적고계모호관계；최후장해모형용우상증고표종합지수화심증고표성분지수적다보예측화창질추세예측。여전형모호시간서렬모형비교，창질추세예측준학솔유교대제고，다보예측결과표명모형구유교호적범화능력。
First, the universe of the discourse is divided into subintervals with the midpoints between two adjacent cluster centers generated by the fuzzy clustering method as their endpoints. And the sub-intervals are employed to fuzzify the time series into fuzzy time series. Then, the fuzzy time series model of high-order fuzzy relationships with multiple fac-tors is built according to the main indexes representing stock price and trading volume. Finally, the model built in this paper is used to perform one-and multi-step forecasting of the daily Shanghai Stock Exchange composite index and Shen-zhen Stock Exchange component index, respectively. Comparing with the benchmark model, one-step forecasting results show that the model improves the prediction accuracy and percent correct of the market up&down trend prediction, and multi-step forecasting results show that the model has good generalization.