Ternary Interval Forecasting of Air Pollutant Concentration: A Novel Multivariate Decomposition and Optimal Variable Weight Ensemble Paradigm

Efficient prediction of air pollutant concentration is of great significance to air pollution prevention, human health protection, and cleaner production. Previous air quality studies mainly focused on point‐based and interval‐based forecasts, and a potential problem with these methods is that some important information may be lost. Therefore, this paper puts forward a novel ternary interval decomposition ensemble paradigm for air pollutant concentration forecasting, which is capable of capturing the daily minimum, daily average, and daily maximum of air pollutant concentration concurrently. In this paradigm, the ternary interval‐valued air pollutant concentration time series (TIAPCTS) is innovatively constructed. Multivariate empirical mode decomposition is first applied to decompose the TIAPCTS into multiple ternary intrinsic mode functions (TIMFs) and one ternary residue (TR). Then, the lower, middle, and upper bounds of each TIMF and TR are simultaneously fitted and predicted by the three‐output multivariate relevance vector machine. To obtain better final outputs, an optimal variable weight ensemble approach is suggested to integrate the forecasting results of TIMFs and TR. The novelty of this study comes from the ternary interval forecasting perspective, multivariate modeling techniques, and weighted ensemble strategy, which not only fully take possible associations among the lower, middle, and upper bounds into account but also improve the modeling efficiency while generating multiple correlated outputs. The proposed paradigm is justified with six kinds of real‐world air pollutant concentration data from Beijing and Shanghai, China, indicating it is a promising alternative for air pollution concentration analysis and forecast.