With hourly PM 2.5 concentration data in Xi’an as sample data, the empirical results demonstrate that our proposed hybrid approach significantly performs better than all benchmarks (including single forecasting techniques and similar approaches with other decomposition) in terms of the accuracy. Finally, these forecasted results are aggregated into an ensemble result as the final forecasting. Next, kernel extreme learning machine (KELM) as a popular machine learning algorithm is employed to forecast all extracted components individually. Then, variational mode decomposition (VMD) is implemented to decompose the denoised data for producing the components. In our proposed approach, wavelet denoising approach, as a noise elimination tool, is applied to remove the noise from the original data. This novel approach is an improved approach under the effective “denoising, decomposition, and ensemble” framework, especially for nonlinear and nonstationary features of PM 2.5 concentration data. To enhance the forecasting accuracy for PM 2.5 concentrations, a novel decomposition-ensemble approach with denoising strategy is proposed in this study.
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