Forecasting of Beijing PM2.5 with a hybrid ARIMA model based on integrated AIC and improved GS fixed-order methods and seasonal decomposition
Lingxiao Zhao, Zhiyang Li, Leilei Qu
Abstract
118.61%, TIC 99.28%, NMAE 98.71%, NMSE 99.97%, OPC 43.13%, MOPC 98.43% and CEC 99.25% compared with the traditional ARIMA model. The results show that the method does greatly improve the prediction performance and provides a convincing tool for policy formulation and governance.
Topics & Concepts
Autoregressive integrated moving averageAkaike information criterionAutoregressive modelMathematicsStatisticsEntropy (arrow of time)Information CriteriaTime seriesMean squared errorEconometricsData miningComputer scienceModel selectionPhysicsQuantum mechanicsAir Quality Monitoring and ForecastingAir Quality and Health ImpactsEnergy, Environment, Economic Growth