A Rough Set Approach to Events Prediction in Multiple Time Series

被引：2

作者：

Gmati, Fatma Ezzahra ^{[1
]}

Chakhar, Salem ^{[2
,3
]}

Chaari, Wided Lejouad ^{[1
]}

Chen, Huijing ^{[2
,3
]}

机构：

[1] Univ Manouba, Natl Sch Comp Sci, COSMOS, Manouba, Tunisia

[2] Univ Portsmouth, Portsmouth Business Sch, Portsmouth, Hants, England

[3] Univ Portsmouth, Ctr Operat Res & Logist, Portsmouth, Hants, England

来源：

RECENT TRENDS AND FUTURE TECHNOLOGY IN APPLIED INTELLIGENCE, IEA/AIE 2018 | 2018年 / 10868卷

关键词：

Event prediction; Multiple time series; Rough sets; Dominance-based Rough Set Approach;

D O I：

10.1007/978-3-319-92058-0_77

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper introduces and illustrates a rough-set based approach to event prediction in multiple time series. The proposed approach uses two different versions of rough set theory to predict events occurrences and intensities. First, classical Indiscernibility relation-based Rough Set Approach (IRSA) is used to predict event classes and occurrences. Then, the Dominance-based Rough Set Approach (DRSA) is employed to predict the intensity of events. This paper presents the fundamental of the proposed approach and the conceptual architecture of a framework implementing this approach.

引用

页码：796 / 807

页数：12

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