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.
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