A fuzzy time-series prediction by GA based rough sets model

Jing Zhao; Junzo Watada; Yoshiyuki Matsumoto

doi:10.1109/ASCC.2015.7244779

A fuzzy time-series prediction by GA based rough sets model

Jing Zhao, Junzo Watada, Yoshiyuki Matsumoto

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

Abstract

Fuzzy time-series (FTS) has been applied to handle non-linear problems, such as enrollment, weather and stock index forecasting. In the forecasting processes, fuzzy logical relation (FLR) plays a pivotal role in forecasting accuracy. Usually FTS uses an equal interval to obtain forecasting values. But in this paper, we use genetic algorithm (GA) to optimize the interval at first. Based on this, then rough set (RS) method is used to recalculate the values. In the empirical analysis, Japan stock index is used as experimental data sets and one fuzzy time-series method, as a comparison model. The experimental results showed that the proposed method is more efficient than the FTS method.

Original language	English
Title of host publication	2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)	9781479978625
DOIs	https://doi.org/10.1109/ASCC.2015.7244779
Publication status	Published - 2015 Sept 8
Event	10th Asian Control Conference, ASCC 2015 - Kota Kinabalu, Malaysia Duration: 2015 May 31 → 2015 Jun 3

Other

Other	10th Asian Control Conference, ASCC 2015
Country/Territory	Malaysia
City	Kota Kinabalu
Period	15/5/31 → 15/6/3

Keywords

Forecasting
Fuzzy time-series
Genetic algorithm
Rough set
Stock Index

ASJC Scopus subject areas

Control and Systems Engineering

Access to Document

10.1109/ASCC.2015.7244779

Cite this

A fuzzy time-series prediction by GA based rough sets model. / Zhao, Jing; Watada, Junzo; Matsumoto, Yoshiyuki.
2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7244779.

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

Zhao, J, Watada, J & Matsumoto, Y 2015, A fuzzy time-series prediction by GA based rough sets model. in 2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015., 7244779, Institute of Electrical and Electronics Engineers Inc., 10th Asian Control Conference, ASCC 2015, Kota Kinabalu, Malaysia, 15/5/31. https://doi.org/10.1109/ASCC.2015.7244779

@inproceedings{0d5643f541654d90b969d724528b6376,

title = "A fuzzy time-series prediction by GA based rough sets model",

abstract = "Fuzzy time-series (FTS) has been applied to handle non-linear problems, such as enrollment, weather and stock index forecasting. In the forecasting processes, fuzzy logical relation (FLR) plays a pivotal role in forecasting accuracy. Usually FTS uses an equal interval to obtain forecasting values. But in this paper, we use genetic algorithm (GA) to optimize the interval at first. Based on this, then rough set (RS) method is used to recalculate the values. In the empirical analysis, Japan stock index is used as experimental data sets and one fuzzy time-series method, as a comparison model. The experimental results showed that the proposed method is more efficient than the FTS method.",

keywords = "Forecasting, Fuzzy time-series, Genetic algorithm, Rough set, Stock Index",

author = "Jing Zhao and Junzo Watada and Yoshiyuki Matsumoto",

year = "2015",

month = sep,

day = "8",

doi = "10.1109/ASCC.2015.7244779",

language = "English",

isbn = "9781479978625",

booktitle = "2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015",

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

note = "10th Asian Control Conference, ASCC 2015 ; Conference date: 31-05-2015 Through 03-06-2015",

}

TY - GEN

T1 - A fuzzy time-series prediction by GA based rough sets model

AU - Zhao, Jing

AU - Watada, Junzo

AU - Matsumoto, Yoshiyuki

PY - 2015/9/8

Y1 - 2015/9/8

N2 - Fuzzy time-series (FTS) has been applied to handle non-linear problems, such as enrollment, weather and stock index forecasting. In the forecasting processes, fuzzy logical relation (FLR) plays a pivotal role in forecasting accuracy. Usually FTS uses an equal interval to obtain forecasting values. But in this paper, we use genetic algorithm (GA) to optimize the interval at first. Based on this, then rough set (RS) method is used to recalculate the values. In the empirical analysis, Japan stock index is used as experimental data sets and one fuzzy time-series method, as a comparison model. The experimental results showed that the proposed method is more efficient than the FTS method.

AB - Fuzzy time-series (FTS) has been applied to handle non-linear problems, such as enrollment, weather and stock index forecasting. In the forecasting processes, fuzzy logical relation (FLR) plays a pivotal role in forecasting accuracy. Usually FTS uses an equal interval to obtain forecasting values. But in this paper, we use genetic algorithm (GA) to optimize the interval at first. Based on this, then rough set (RS) method is used to recalculate the values. In the empirical analysis, Japan stock index is used as experimental data sets and one fuzzy time-series method, as a comparison model. The experimental results showed that the proposed method is more efficient than the FTS method.

KW - Forecasting

KW - Fuzzy time-series

KW - Genetic algorithm

KW - Rough set

KW - Stock Index

UR - http://www.scopus.com/inward/record.url?scp=84957615660&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84957615660&partnerID=8YFLogxK

U2 - 10.1109/ASCC.2015.7244779

DO - 10.1109/ASCC.2015.7244779

M3 - Conference contribution

AN - SCOPUS:84957615660

SN - 9781479978625

BT - 2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 10th Asian Control Conference, ASCC 2015

Y2 - 31 May 2015 through 3 June 2015

ER -

A fuzzy time-series prediction by GA based rough sets model

Abstract

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this