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Handling forecasting problems based on high-order fuzzy logical relationships

机译：基于高阶模糊逻辑关系的预测问题处理

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摘要

People usually use many methods to predict the weather, the temperature, the stock index, the enrollments, the earthquake, the economy, etc. Based on these forecasting results, people can prevent damages to occur or get benefits from the forecasting activities. In this paper, we present a new method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the enrollments of the University of Alabama and the inventory demand based on high-order fuzzy logical relationships. First, the proposed method fuzzifies the historical data into fuzzy sets to form high-order fuzzy logical relationships. Then, it calculates the value of the variable between the subscripts of adjacent fuzzy sets appearing in the antecedents of high-order fuzzy logical relationships. Then, it lets the high-order fuzzy logical relationships with the same variable value form a high-order fuzzy logical relationship group. Finally, it chooses a high-order fuzzy logical relationship group to forecast the TAIEX. The proposed method gets a higher average forecasting accuracy rate to forecast the TAIEX, the enrollments of the University of Alabama and the inventory demand than the existing methods.
机译：人们通常使用多种方法来预测天气，温度，股票指数，入学人数，地震，经济状况等。基于这些预测结果，人们可以防止发生损害或从预测活动中受益。在本文中，我们提出了一种基于高阶模糊逻辑关系预测台湾证券交易所资本加权股票指数（TAIEX），阿拉巴马大学的入学率和库存需求的新方法。首先，提出的方法将历史数据模糊化为模糊集合，以形成高阶模糊逻辑关系。然后，它计算在高阶模糊逻辑关系的先例中出现的相邻模糊集的下标之间的变量值。然后，让具有相同变量值的高阶模糊逻辑关系形成一个高阶模糊逻辑关系组。最后，选择一个高阶模糊逻辑关系组来预测TAIEX。与现有方法相比，该方法具有较高的平均预测准确率，可用于预测TAIEX，阿拉巴马大学的入学人数和库存需求。

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