【摘要】 文章研究了模糊逻辑模型在金融预测领域中的应用。由于该模型自身的局限性,在对金融时间序列趋势的连续预测应用中,趋势准确率偏低,连续预测值波动小(体现不出未来的市场走向),对此,提出了模糊修正的方法。文章运用模糊修正模型对上证综合指数和道琼斯平均工业指数做试验,并与BP神经网络进行比较,试验结果表明,运用模糊修正模型进行金融预测是可行的和有效的。更多还原

【Abstract】 The application of fuzzy logic model in the prediction of financial time series is investigated.For the shortage of the original fuzzy logic model,when using it for trend forecasting,the trend accuracy ratio is low and the consecutive predicting values fluctuate flatly,which cannot reflect where the market will make for.In order to overcome the deficiency,a fuzzy revising method is presented,that is revising the original predicting values by using the secondary predicting ones.This study examines the feasibility of fuzzy modified model in financial forecasting by comparing it with BP neural networks.The experimental results show that using fuzzy modified model for financial prediction is effective and feasible.更多还原