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

Yoshiyuki Matsumoto; Junzo Watada

doi:10.1109/SCIS-ISIS.2014.7044842

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

Yoshiyuki Matsumoto, Junzo Watada

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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

Original language	English
Title of host publication	2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1378-1381
Number of pages	4
ISBN (Print)	9781479959556
DOIs	https://doi.org/10.1109/SCIS-ISIS.2014.7044842
Publication status	Published - 2014 Feb 18
Event	2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014 - Kitakyushu, Japan Duration: 2014 Dec 3 → 2014 Dec 6

Other

Other	2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014
Country/Territory	Japan
City	Kitakyushu
Period	14/12/3 → 14/12/6

Keywords

knowledge acuisition
rough sets
time series data

ASJC Scopus subject areas

Software
Artificial Intelligence

Access to Document

10.1109/SCIS-ISIS.2014.7044842

Cite this

Matsumoto, Y., & Watada, J. (2014). Analysis using rough set of time series data including a large variation. In 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014 (pp. 1378-1381). [7044842] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SCIS-ISIS.2014.7044842

Analysis using rough set of time series data including a large variation. / Matsumoto, Yoshiyuki; Watada, Junzo.

2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 1378-1381 7044842.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Matsumoto, Y & Watada, J 2014, Analysis using rough set of time series data including a large variation. in 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014., 7044842, Institute of Electrical and Electronics Engineers Inc., pp. 1378-1381, 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014, Kitakyushu, Japan, 14/12/3. https://doi.org/10.1109/SCIS-ISIS.2014.7044842

Matsumoto Y, Watada J. Analysis using rough set of time series data including a large variation. In 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 1378-1381. 7044842 doi: 10.1109/SCIS-ISIS.2014.7044842

@inproceedings{6ec8ae9c57814f768bd559cab0fe04fa,

title = "Analysis using rough set of time series data including a large variation",

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

keywords = "knowledge acuisition, rough sets, time series data",

author = "Yoshiyuki Matsumoto and Junzo Watada",

year = "2014",

month = feb,

day = "18",

doi = "10.1109/SCIS-ISIS.2014.7044842",

language = "English",

isbn = "9781479959556",

pages = "1378--1381",

booktitle = "2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014 ; Conference date: 03-12-2014 Through 06-12-2014",

}

TY - GEN

T1 - Analysis using rough set of time series data including a large variation

AU - Matsumoto, Yoshiyuki

AU - Watada, Junzo

PY - 2014/2/18

Y1 - 2014/2/18

N2 - 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.

AB - 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.

KW - knowledge acuisition

KW - rough sets

KW - time series data

UR - http://www.scopus.com/inward/record.url?scp=84946530761&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84946530761&partnerID=8YFLogxK

U2 - 10.1109/SCIS-ISIS.2014.7044842

DO - 10.1109/SCIS-ISIS.2014.7044842

M3 - Conference contribution

AN - SCOPUS:84946530761

SN - 9781479959556

SP - 1378

EP - 1381

BT - 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014

Y2 - 3 December 2014 through 6 December 2014

ER -

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

Abstract

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this