title: A novel hybrid model for air quality index forecasting based on two-phase decomposition technique and modified extreme learning machine.
abstract: The randomness, non-stationarity and irregularity of air quality index (AQI) series bring the difficulty of AQI forecasting. To enhance forecast accuracy, a novel hybrid forecasting model combining two-phase decomposition technique and extreme learning machine (ELM) optimized by differential evolution (DE) algorithm is developed for AQI forecasting in this paper. In phase I, the complementary ensemble empirical mode decomposition (CEEMD) is utilized to decompose the AQI series into a set of intrinsic mode functions (IMFs) with different frequencies; in phase II, in order to further handle the high frequency IMFs which will increase the forecast difficulty, variational mode decomposition (VMD) is employed to decompose the high frequency IMFs into a number of variational modes (VMs). Then, the ELM model optimized by DE algorithm is applied to forecast all the IMFs and VMs. Finally, the forecast value of each high frequency IMF is obtained through adding up the forecast results of all corresponding VMs, and the forecast series of AQI is obtained by aggregating the forecast results of all IMFs. To verify and validate the proposed model, two daily AQI series from July 1, 2014 to June 30, 2016 collected from Beijing and Shanghai located in China are taken as the test cases to conduct the empirical study. The experimental results show that the proposed hybrid model based on two-phase decomposition technique is remarkably superior to all other considered models for its higher forecast accuracy.
authors.affiliation: School of Economics and Management, China University of Geosciences, Wuhan 430074, China; Mineral Resource Strategy and Policy Research Center, China University of Geosciences, Wuhan 430074, China; Université de Bourgogne Franche-Comté, UTBM, IRTES, Rue Thierry Mieg, 90010 Belfort cedex, France. Electronic address: wang.deyun@hotmail.com.
authors.forename: Deyun
authors.initials: D
authors.lastname: Wang
authors.affiliation: School of Economics and Management, China University of Geosciences, Wuhan 430074, China; Mineral Resource Strategy and Policy Research Center, China University of Geosciences, Wuhan 430074, China.
authors.forename: Shuai
authors.initials: S
authors.lastname: Wei
authors.affiliation: School of Economics and Management, China University of Geosciences, Wuhan 430074, China; Mineral Resource Strategy and Policy Research Center, China University of Geosciences, Wuhan 430074, China.
authors.forename: Hongyuan
authors.initials: H
authors.lastname: Luo
authors.affiliation: School of Economics and Management, China University of Geosciences, Wuhan 430074, China; Mineral Resource Strategy and Policy Research Center, China University of Geosciences, Wuhan 430074, China.
authors.forename: Chenqiang
authors.initials: C
authors.lastname: Yue
authors.affiliation: Université de Bourgogne Franche-Comté, UTBM, IRTES, Rue Thierry Mieg, 90010 Belfort cedex, France.
authors.forename: Olivier
authors.initials: O
authors.lastname: Grunder
keywords: Air quality index (AQI); Complementary ensemble empirical mode decomposition (CEEMD); Differential evolution (DE); Extreme learning machine (ELM); Variational mode decomposition (VMD)