A Decomposition-Ensemble Approach with Denoising Strategy for PM2.5 Concentration Forecasting

To enhance the forecasting accuracy for PM2.5 concentrations, a novel decomposition-ensemble approach with denoising strategy is proposed in this study. This novel approach is an improved approach under the effective denoising, decomposition, and ensemble framework, especially for nonlinear and nonstationary features of PM2.5 concentration data. In our proposed approach, wavelet denoising approach, as a noise elimination tool, is applied to remove the noise from the original data. Then, variational mode decomposition (VMD) is implemented to decompose the denoised data for producing the components. Next, kernel extreme learning machine (KELM) as a popular machine learning algorithm is employed to forecast all extracted components individually. Finally, these forecasted results are aggregated into an ensemble result as the final forecasting. With hourly PM2.5 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. Consequently, the robustness results also indicate that our proposed hybrid approach can be recommended as a promising forecasting tool for capturing and exploring the complicated time series data.

Automated summary

A novel decomposition-ensemble approach with denoising strategy is proposed to enhance the forecasting accuracy for PM2.5 concentrations. Empirical results demonstrate that this hybrid method significantly outperforms benchmarks, making it a promising forecasting tool for capturing and exploring complex time series data.