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

研究成果: Conference contribution

抄録

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.

本文言語	English
ホスト出版物のタイトル	2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015
出版社	Institute of Electrical and Electronics Engineers Inc.
ISBN（印刷版）	9781479978625
DOI	https://doi.org/10.1109/ASCC.2015.7244779
出版ステータス	Published - 2015 9月 8
イベント	10th Asian Control Conference, ASCC 2015 - Kota Kinabalu, Malaysia 継続期間: 2015 5月 31 → 2015 6月 3

Other

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

ASJC Scopus subject areas

制御およびシステム工学

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10.1109/ASCC.2015.7244779

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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

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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",

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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",

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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

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DO - 10.1109/ASCC.2015.7244779

M3 - Conference contribution

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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

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Other

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