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JACIII Vol.21 No.6 pp. 1026-1033
doi: 10.20965/jaciii.2017.p1026
(2017)

Paper:

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Analysis of Time-Series Data by Merging Decision Rules

Yoshiyuki Matsumoto* and Junzo Watada**

*Shimonoseki City University
2-1-1 Daigaku-cho, Shimonoseki, Yamaguchi 751-8510, Japan

**Waseda University
2-7 Hibikino, Wakamatsu, Kitakyushu, Fukuoka 808-0135, Japan

Received:
January 4, 2017
Accepted:
April 20, 2017
Published:
October 20, 2017
Creative Commons License
Keywords:
rough sets, decision rules, time-series data, knowledge acquisition
Abstract

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.

Cite this article as:
Y. Matsumoto and J. Watada, “Analysis of Time-Series Data by Merging Decision Rules,” J. Adv. Comput. Intell. Intell. Inform., Vol.21 No.6, pp. 1026-1033, 2017.
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Last updated on Apr. 22, 2024