Analysis of Time-Series Data by Merging Decision Rules | Zendy

Yoshiyuki Matsumoto | Zendy; Junzo Watada | Zendy

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Analysis of Time-Series Data by Merging Decision Rules

Author(s) -

Yoshiyuki Matsumoto,

Junzo Watada

Publication year - 2017

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2017.p1026

Subject(s) - rough set , computer science , data mining , decision rule , time series , series (stratigraphy) , set (abstract data type) , dominance based rough set approach , data set , decision table , machine learning , artificial intelligence , paleontology , biology , programming language

Rough set theory was proposed by Z. Pawlak in 1982. This theory enables the mining of knowledge granules as decision rules from a database, the web, and other sources. This decision rule set can then be used for data analysis. We can apply the decision rule set to reason, estimate, evaluate, or forecast an unknown object. In this paper, rough set theory is used for the analysis of time-series data. We propose a method to acquire rules from time-series data using regression. The trend of the regression line can be used as a condition attribute. We predict the future slope of the time-series data as decision attributes. We also use merging rules to further analyze the time series data.

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