A novel multi-factor & multi-scale method for PM2.5 concentration forecasting

In the era of big data, a variety of factors (particularly meteorological factors) have been applied to PM2.5 concentration prediction, revealing a clear discrepancy in timescale. To capture the complicated multi-scale relationship with PM2.5-related factors, a novel multi-factor & multi-scale method is proposed for PM2.5 forecasting. Three major steps are taken: (1) multi-factor analysis, to select predictive factors via statistical tests; (2) multi-scale analysis, to extract scale-aligned components via multivariate empirical mode decomposition; and (3) PM2.5 prediction, including individual prediction at each timescale and ensemble prediction across different timescales. The empirical study focuses on the PM2.5 of Cangzhou, which is one of the most air-polluted cities in China, and indicates that the proposed multi-factor & multi-scale learning paradigms statistically outperform their corresponding original techniques (without multi-factor and multi-scale analysis), semi-improved variants (with either multi-factor or multi-scale analysis), and similar counterparts (with other multi-scale analyses) in terms of prediction accuracy. General framework of the novel multi-factor & multi-scale methodology for PM2.5 forecasting. [Display omitted] •A multi-factor & multi-scale method is proposed to forecast PM2.5 concentration.•It includes 3 steps: multi-factor analysis, multi-scale analysis and prediction.•Empirical study verifies its superiority over both popular and similar benchmarks.•Meteorological factors are shown to largely drive PM2.5 on different timescales.•Analyzing multi-scale relationship of PM2.5 and these factors enhances accuracy. By carefully analysing the multi-scale relationship between PM2.5 and related factors, the novel multi-factor & multi-scale method largely improves the prediction accuracy for PM2.5.