A Decomposition-Ensemble Approach with Denoising Strategy for PM 2.5 Concentration Forecasting

[摘要] To enhance the forecasting accuracy for PM 2.5 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 PM 2.5 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 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. 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.

[学科分类] 安全、风险、质量和可靠性