Knowledge acquisition from time-series data with similar using the rough sets

Abstract

Rough set theory was proposed by Z.Pawlak in 1982. This theory can mine knowledge granules through a decision rule from a database, a web base, a set and so on. The decision rule is used for data analysis as well. And we can apply the decision rule to reason, estimate, evaluate, or forecast an unknown object. In this paper, the rough set theory is used to analysis of time series data. Knowledge granules are minded from the data set of tick-wise price fluctuations. We search for the law of similarity from time-series data using the rough sets.