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

In the era of big data, a variety of factors (particularly meteorological factors) have been applied to PM concentration prediction, revealing a clear discrepancy in timescale. To capture the complicated multi-scale relationship with PM-related factors, a novel multi-factor & multi-scale method is proposed for PM 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) PM prediction, including individual prediction at each timescale and ensemble prediction across different timescales. The empirical study focuses on the PM 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.