Linear and nonlinear framework for interval-valued PM2.5 concentration forecasting based on multi-factor interval division strategy and bivariate empirical mode decomposition
作者：
Highlights：
• Enhanced linear and nonlinear framework for interval-valued time series is proposed.
• Multi-step-ahead forecasting for decomposition-ensemble method is performed.
• Multi-factor interval division can mine the internal information of the interval.
• Bivariate decomposition algorithm is introduced for multi-scale modeling.
• The proposed model outperforms other state-of-the-art methods.
摘要
•Enhanced linear and nonlinear framework for interval-valued time series is proposed.•Multi-step-ahead forecasting for decomposition-ensemble method is performed.•Multi-factor interval division can mine the internal information of the interval.•Bivariate decomposition algorithm is introduced for multi-scale modeling.•The proposed model outperforms other state-of-the-art methods.
论文关键词：Linear and nonlinear framework,Bivariate empirical mode decomposition,Interval division,Multivariate relevance vector machine,interval-valued PM2.5 forecasting
论文评审过程：Received 7 November 2021, Revised 14 April 2022, Accepted 29 May 2022, Available online 2 June 2022, Version of Record 6 June 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.117707
