Developing intuitionistic fuzzy seasonality regression with particle swarm optimization for air pollution forecasting

ASIA unversity > 管理學院 > 經營管理學系 > 期刊論文 > Item 310904400/111665

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://asiair.asia.edu.tw/ir/handle/310904400/111665

题名:	Developing intuitionistic fuzzy seasonality regression with particle swarm optimization for air pollution forecasting
作者:	Ho, Chung-Ha;Ho, Chung-Han;Chang, Ping-;Chang, Ping-Teng;Hung, Kuo-Ch;Hung, Kuo-Chen;林國平;Lin, Kuo-Ping
贡献者:	管理學院經營管理學系
日期:	2018-09
上传时间:	2018-12-24 09:21:54 (UTC+0)
摘要:	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.
關聯:	INDUSTRIAL MANAGEMENT & DATA SYSTEMS
显示于类别:	[經營管理學系 ] 期刊論文

文件中的档案:

没有与此文件相关的档案.

在ASIAIR中所有的数据项都受到原著作权保护.

数据加载中.....