Analysis using rough set of time series data including a large variation

被引：0

作者：

Matsumoto, Yoshiyuki ^{[1
]}

Watada, Junzo ^{[2
]}

机构：

[1] Shimonoseki City Univ, Fac Econ, Shimonoseki, Yamaguchi, Japan

[2] Waseda Univ, Grad Sch Informat Prod & Syst, Kitakyushu, Fukuoka, Japan

来源：

2014 JOINT 7TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS (SCIS) AND 15TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS (ISIS) | 2014年

关键词：

rough sets; time series data; knowledge acuisition;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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 acquire knowledge from the time-series data including large variation. And we compare the data including large variation and normal data.

引用

页码：1378 / 1381

页数：4

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