A steady-state probabilities model for fuzzy time series forecasting

Shu Ching Kuo, Chih Chuan Chen, Hsuan Yu Chen, Sheng Tun Li, Hung Jen Wang

研究成果: Conference contribution

4 引文 (Scopus)

摘要

In this time of big data, many people often using statistical or mathematical models to analyze historical data and use data analysis to predict the changes of future. In past studies, regression analysis, neural network and logistic regression models, etc., are often used to analyze. But the above methods are based on numerical data to accurately predict, for the fuzzy data, these methods cannot be used to analysis. Until 1965, Zadeh proposed the fuzzy theory, the linguistic variable finally can be analyzed and discussion. Currently, the fuzzy theory has been widely used in many fields, such as fuzzy time series, fuzzy regression, etc. For enhancing the prediction accuracy, there are still some issues in the study of fuzzy time series. In this study, we combine the fuzzy theory and the Markov theory, using Markov matrix instead of the traditional fuzzy relation matrix, consider the frequency of transfer status. Calculate the steady-state probabilities. Build a whole new predict model and enhance the prediction accuracy of the results. Finally, to justify the effeteness of the proposed forecasting model, we compare and analyze on prediction accuracy with real-world data sets.

原文English
主出版物標題Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016
編輯Ayako Hiramatsu, Tokuro Matsuo, Akimitsu Kanzaki, Norihisa Komoda
發行者Institute of Electrical and Electronics Engineers Inc.
頁面615-619
頁數5
ISBN(電子)9781467389853
DOIs
出版狀態Published - 2016 八月 31
事件5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 - Kumamoto, Japan
持續時間: 2016 七月 102016 七月 14

出版系列

名字Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016

Other

Other5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016
國家Japan
城市Kumamoto
期間16-07-1016-07-14

指紋

Time series
Linguistics
Regression analysis
Logistics
Mathematical models
Neural networks

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

引用此文

Kuo, S. C., Chen, C. C., Chen, H. Y., Li, S. T., & Wang, H. J. (2016). A steady-state probabilities model for fuzzy time series forecasting. 於 A. Hiramatsu, T. Matsuo, A. Kanzaki, & N. Komoda (編輯), Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 (頁 615-619). [7557685] (Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IIAI-AAI.2016.137
Kuo, Shu Ching ; Chen, Chih Chuan ; Chen, Hsuan Yu ; Li, Sheng Tun ; Wang, Hung Jen. / A steady-state probabilities model for fuzzy time series forecasting. Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. 編輯 / Ayako Hiramatsu ; Tokuro Matsuo ; Akimitsu Kanzaki ; Norihisa Komoda. Institute of Electrical and Electronics Engineers Inc., 2016. 頁 615-619 (Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016).
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abstract = "In this time of big data, many people often using statistical or mathematical models to analyze historical data and use data analysis to predict the changes of future. In past studies, regression analysis, neural network and logistic regression models, etc., are often used to analyze. But the above methods are based on numerical data to accurately predict, for the fuzzy data, these methods cannot be used to analysis. Until 1965, Zadeh proposed the fuzzy theory, the linguistic variable finally can be analyzed and discussion. Currently, the fuzzy theory has been widely used in many fields, such as fuzzy time series, fuzzy regression, etc. For enhancing the prediction accuracy, there are still some issues in the study of fuzzy time series. In this study, we combine the fuzzy theory and the Markov theory, using Markov matrix instead of the traditional fuzzy relation matrix, consider the frequency of transfer status. Calculate the steady-state probabilities. Build a whole new predict model and enhance the prediction accuracy of the results. Finally, to justify the effeteness of the proposed forecasting model, we compare and analyze on prediction accuracy with real-world data sets.",
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Kuo, SC, Chen, CC, Chen, HY, Li, ST & Wang, HJ 2016, A steady-state probabilities model for fuzzy time series forecasting. 於 A Hiramatsu, T Matsuo, A Kanzaki & N Komoda (編輯), Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016., 7557685, Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016, Institute of Electrical and Electronics Engineers Inc., 頁 615-619, 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016, Kumamoto, Japan, 16-07-10. https://doi.org/10.1109/IIAI-AAI.2016.137

A steady-state probabilities model for fuzzy time series forecasting. / Kuo, Shu Ching; Chen, Chih Chuan; Chen, Hsuan Yu; Li, Sheng Tun; Wang, Hung Jen.

Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. 編輯 / Ayako Hiramatsu; Tokuro Matsuo; Akimitsu Kanzaki; Norihisa Komoda. Institute of Electrical and Electronics Engineers Inc., 2016. p. 615-619 7557685 (Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016).

研究成果: Conference contribution

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AU - Kuo, Shu Ching

AU - Chen, Chih Chuan

AU - Chen, Hsuan Yu

AU - Li, Sheng Tun

AU - Wang, Hung Jen

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N2 - In this time of big data, many people often using statistical or mathematical models to analyze historical data and use data analysis to predict the changes of future. In past studies, regression analysis, neural network and logistic regression models, etc., are often used to analyze. But the above methods are based on numerical data to accurately predict, for the fuzzy data, these methods cannot be used to analysis. Until 1965, Zadeh proposed the fuzzy theory, the linguistic variable finally can be analyzed and discussion. Currently, the fuzzy theory has been widely used in many fields, such as fuzzy time series, fuzzy regression, etc. For enhancing the prediction accuracy, there are still some issues in the study of fuzzy time series. In this study, we combine the fuzzy theory and the Markov theory, using Markov matrix instead of the traditional fuzzy relation matrix, consider the frequency of transfer status. Calculate the steady-state probabilities. Build a whole new predict model and enhance the prediction accuracy of the results. Finally, to justify the effeteness of the proposed forecasting model, we compare and analyze on prediction accuracy with real-world data sets.

AB - In this time of big data, many people often using statistical or mathematical models to analyze historical data and use data analysis to predict the changes of future. In past studies, regression analysis, neural network and logistic regression models, etc., are often used to analyze. But the above methods are based on numerical data to accurately predict, for the fuzzy data, these methods cannot be used to analysis. Until 1965, Zadeh proposed the fuzzy theory, the linguistic variable finally can be analyzed and discussion. Currently, the fuzzy theory has been widely used in many fields, such as fuzzy time series, fuzzy regression, etc. For enhancing the prediction accuracy, there are still some issues in the study of fuzzy time series. In this study, we combine the fuzzy theory and the Markov theory, using Markov matrix instead of the traditional fuzzy relation matrix, consider the frequency of transfer status. Calculate the steady-state probabilities. Build a whole new predict model and enhance the prediction accuracy of the results. Finally, to justify the effeteness of the proposed forecasting model, we compare and analyze on prediction accuracy with real-world data sets.

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PB - Institute of Electrical and Electronics Engineers Inc.

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Kuo SC, Chen CC, Chen HY, Li ST, Wang HJ. A steady-state probabilities model for fuzzy time series forecasting. 於 Hiramatsu A, Matsuo T, Kanzaki A, Komoda N, 編輯, Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 615-619. 7557685. (Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016). https://doi.org/10.1109/IIAI-AAI.2016.137