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首页> 外文期刊>Journal of Computational Methods in Sciences and Engineering >Relative distance case-based reasoning for international oil price fluctuation early warning
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Relative distance case-based reasoning for international oil price fluctuation early warning

机译：基于相对距离案例推理的国际油价波动预警

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摘要

This paper addresses a new method of international oil price fluctuation warning using case-based reasoning (CBR). The aim of this work presented here is to provide effective warning knowledge for decision-makers. At first, we design the similarity calculation methods according to the different case feature, such as crisp number, interval number, crisp symbols and fuzzy linguistic variables. The similarity of each feature is calculated between target case and each historical case, which step gets a similarity matrix. The CBR system that employs relative distance measure model with the technique for order preference by similarity to an ideal solution (TOPSIS) in the ensemble frame is named as relative distance case-based reasoning (RDCBR). At the same time, we introduce RDCBR in international oil price fluctuation prediction and analyze the obtained results of oil price fluctuation prediction, comparing them with those provided by the other two well-known CBR models with Euclidean distance (ECBR) and Manhuttan distance (MCBR) as its heart of retrieval. Empirical results indicate that RDCBR outperforms ECBR, MCBR, which can effectively improve the accuracy of CBR system.
机译：本文提出了一种基于案例推理的国际油价波动预警新方法。此处提出的这项工作的目的是为决策者提供有效的警告知识。首先，针对不同的案例特征，设计了相似度计算方法，如明晰数，区间数，明晰符号和模糊语言变量。计算目标案例和每个历史案例之间的每个特征的相似度，此步骤将获得一个相似度矩阵。将采用相对距离度量模型和与整体框架中的理想解决方案（TOPSIS）相似的顺序偏爱技术的CBR系统称为基于相对距离案例推理（RDCBR）。同时，我们将RDCBR引入国际油价波动预测中，并对获得的油价波动预测结果进行分析，并将其与其他两个著名的CBR模型提供的欧氏距离（ECBR）和曼胡坦距离（MCBR）进行比较。 ）作为检索的心脏。实验结果表明，RDCBR的性能优于ECBR，MCBR，可有效提高CBR系统的精度。

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