ASIA unversity:Item 310904400/111665
English  |  正體中文  |  简体中文  |  Items with full text/Total items : 90570/105786 (86%)
Visitors : 16304248      Online Users : 222
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version


    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/111665


    Title: Developing intuitionistic fuzzy seasonality regression with particle swarm optimization for air pollution forecasting
    Authors: Ho, Chung-Ha;Ho, Chung-Han;Chang, Ping-;Chang, Ping-Teng;Hung, Kuo-Ch;Hung, Kuo-Chen;林國平;Lin, Kuo-Ping
    Contributors: 管理學院經營管理學系
    Date: 2018-09
    Issue Date: 2018-12-24 17:21:54 (UTC+8)
    Abstract: Published in Industrial Management and Data Systems 2019
    DOI:10.1108/IMDS-02-2018-0063
    Purpose




    The purpose of this paper is to develop a novel intuitionistic fuzzy seasonality regression (IFSR) with particle swarm optimization (PSO) algorithms to accurately forecast air pollutions, which are typical seasonal time series data. Seasonal time series prediction is a critical topic, and some time series data contain uncertain or unpredictable factors. To handle such seasonal factors and uncertain forecasting seasonal time series data, the proposed IFSR with the PSO method effectively extends the intuitionistic fuzzy linear regression (IFLR).




    Design/methodology/approach




    The prediction model sets up IFLR with spreads unrestricted so as to correctly approach the trend of seasonal time series data when the decomposition method is used. PSO algorithms were simultaneously employed to select the parameters of the IFSR model. In this study, IFSR with the PSO method was first compared with fuzzy seasonality regression, providing evidence that the concept of the intuitionistic fuzzy set can improve performance in forecasting the daily concentration of carbon monoxide (CO). Furthermore, the risk management system also implemented is based on the forecasting results for decision-maker.




    Findings




    Seasonal autoregressive integrated moving average and deep belief network were then employed as comparative models for forecasting the daily concentration of CO. The empirical results of the proposed IFSR with PSO model revealed improved performance regarding forecasting accuracy, compared with the other methods.




    Originality/value




    This study presents IFSR with PSO to accurately forecast air pollutions. The proposed IFSR with PSO model can efficiently provide credible values of prediction for seasonal time series data in uncertain environments.
    Relation: INDUSTRIAL MANAGEMENT & DATA SYSTEMS
    Appears in Collections:[Department of Business Administration] Journal Article

    Files in This Item:

    There are no files associated with this item.



    All items in ASIAIR are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback