A novel dynamic ensemble air quality index forecasting system

The air quality index (AQI) can reflect the change of air quality in real time. It has linear characteristics, nonlinear and fuzzy features. However, a single model cannot fit the dynamic changes of AQI scientifically and reasonably. Therefore, this paper proposes a new dynamic ensemble forecasting system based on multi-objective intelligent optimization algorithm to forecast AQI, which has time-varying parameter weights and mainly contains three module: data preprocessing module, dynamic integration forecasting module and system evaluation module. In the data preprocessing module, the off-line frequency domain filtering approach is applied to identify and correct the outliers in the series. To better extract the series information and remove the random noise, the time series is decomposed into multi-level utilizing decomposition strategy and reconstructed. In the dynamic integration forecasting module, three hybrid models based on ARIMA, optimized extreme learning machine and fuzzy time series model, named as HCA, HCME and HCFL respectively, are used to forecast the reconstructed series and time varying parameters are employed to dynamically combine the forecasting results. In the system evaluation module, the accuracy of the system was tested by parameter test method and non-parametric test method respectively. The results demonstrate that the proposed dynamic integrated model is not only superior to other comparison models in forecasting accuracy, but also provides strong technical support for air quality forecasting and treatment.