Affiliations: School of Management, Chongqing University of Technology, Chongqing, China
Correspondence:
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Corresponding author: Jian Hu, School of Management, Chongqing University of Technology, No. 69, Red Road, Banan District, Chongqing 400054, China. E-mail:[email protected]
Abstract: 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.
