A forecasting system for deterministic and uncertain prediction of air pollution data. (1st December 2022) - Ding, Zhenni; Chen, Huayou; Zhou, Ligang; Wang, Zicheng
Abstract: Air quality forecasting has practical implications for policy-makers guiding pollution control at early stages. However, due to the complexity and nonlinearity of air quality data, it is difficult to improve the forecasting precision of air quality indices by a single forecasting model. Accordingly, a novel combined model integrating decomposition and reconstructing techniques and multiple individual models is proposed to produce both point and interval forecasts for air quality data. In the devised model, first, the original series is decomposed into several independent intrinsic mode functions and a residue by an algorithm based on the complete ensemble empirical mode decomposition method. Then, the obtained modes are reconstructed into components by an improved version of the run-length judgment method and the number of reconstructed components is optimized to make a tradeoff between a higher forecasting accuracy and a lower workload in the designed system. To forecast different frequency components, various individual models are utilized. Finally, according to the results of point forecasts, the distribution of forecasting error is analyzed and interval forecasts are developed by the quantile regression method to reflect more uncertain information about the analyzed air pollution data. The concentration series of four major air pollutants in Hefei are selected as examples to test the robustness and effectiveness of the proposed combined model. The results indicate that, on the one hand, the established combined model outperforms three individual models and five hybrid models, and on the other hand, it can offer more valuable suggestions for policy-makers. Highlights: A combined model for forecasting different air pollutant is proposed. An improved version of run-length judgment reconstruction method is developed. The combined model outperforms multiple models significantly.