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An expanded Adaptive Neuro-Fuzzy Inference System (ANFIS) model based on AR and causality of multi-nation stock market volatility for TAIEX forecasting

机译:基于AR和多国股票市场波动性的因果关系的扩展自适应神经模糊推理系统(ANFIS)模型用于TAIEX预测

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摘要

For common people, stock investing is one popular way to manage their property. As Information Technology (IT) has risen in recent years, every security company has analyzed computer systems for their customers by developing their own investing. Taiwan is an island nation, and the economy relies on international trade deeply. The fluctuations of international stock markets will impact Taiwan stock market to a certain degree. Therefore, the use of fluctuations of other stock markets as forecasting factors for forecasting the Taiwan stock market is a practical way. In this paper, the proposed model uses the fluctuations of other national stock markets as forecasting factors, employs different discretization methods (Fuzzy C-means clustering, Subtractive Clustering and Cumulative Probability Distribution Approach) to discretize stock data, utilizes a fuzzy inference system to produce understandable rules, and applies an adaptive neural network to optimize model parameters to reach the best forecasting accuracy. To evaluate the forecasting performances, the proposed model is compared with two different models, Chen’s model and Yu’s model. The experimental results indicate that the proposed model is superior to the listing methods in terms of RMSE (root mean squared error).
机译:对于普通人来说,股票投资是管理其财产的一种流行方法。随着信息技术(IT)近年来的兴起,每家安全公司都通过开发自己的投资来为客户分析计算机系统。台湾是一个岛国,经济十分依赖国际贸易。国际股市的波动将在一定程度上影响台湾股市。因此,利用其他股票市场的波动作为预测台湾股票市场的预测因素是一种实用的方法。在本文中,该模型使用其他国家股票市场的波动作为预测因素,采用不同的离散化方法(模糊C均值聚类,减法聚类和累积概率分布方法)对股票数据进行离散化,并利用模糊推理系统进行生产。可理解的规则,并应用自适应神经网络来优化模型参数,以达到最佳的预测精度。为了评估预测效果,将建议的模型与两种不同的模型进行比较,分别是Chen模型和Yu模型。实验结果表明,在RMSE(均方根误差)方面,该模型优于列表方法。

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