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粗糙集理论是一种处理不确定知识的数学工具,现在粗糙集理论大多数的研究应用都停留在静态表的基础上,但在实际中决策信息表的数据是在不停的增加更新当中,静态的方法在处理不停增加和变换的数据时有着很明显的局限性。本文着眼于研究多粒度时间序列下各个粒度所产生的决策间的相互关联性,并建立了相关的演化模型。同时利用回归分析的方法设计了预测算法。
Rough set theory is a mathematical tool to deal with uncertain knowledge. Nowadays, most research and application of rough set theory are based on static table. However, in reality, the data of decision table is constantly updated, The static approach has obvious limitations in dealing with data that is continually increasing and transforming. This paper focuses on the interdependence of the decisions made by the various granularities under the multi-granularity time series and establishes the related evolution models. At the same time, the forecast algorithm is designed by regression analysis.