Analyzing　and　modeling　passenger　demand　dynamic,　which　has　important　implications　on　the　management　and　the　operation　in　the　entire　aviation　industry,　are　deemed　to　be　a　tough　challenge.　Air　passenger　demand,　however,　exhibits　consistently　complex　non-linearity　and　non-stationarity.　To　capture　more　precisely　the　aforementioned　complex　behavior,　this　paper　proposes　a　hybrid　approach　VMD-ARMA/KELM-KELM　for　the　short-term　forecasting,　which　consists　of　variational　mode　decomposition　(VMD),　autoregressive　moving　average　model　(ARMA)　and　kernel　extreme　learning　machine　(KELM).　First,　VMD　is　adopted　to　decompose　the　original　data　into　several　mode　functions　so　as　to　reduce　their　complexity.　Then,　the　unit　root　test　(ADF　test)　is　employed　to　classify　all　the　modes　into　the　stable　and　unstable　series.　Meanwhile,　the　ARMA　and　the　KELM　models　are　used　to　forecast　both　the　stationary　and　non-stationary　components,　respectively.　Lastly,　the　final　result　is　integrated　by　another　KELM　model　incorporating　the　forecasting　results　of　all　components.　In　order　to　prove　and　verify　the　feasibility　and　robustness　of　the　proposed　approach,　the　passenger　demands　of　Beijing,　Guangzhou　and　Pudong　airports　are　introduced　to　test　the　performance.　Also,　the　experimental　results　show　that　the　novel　approach　does　have　a　more　obviously　advantage　than　other　benchmark　models　regarding　both　accuracy　and　robustness　analysis.　Therefore,　this　approach　can　be　utilized　as　a　convincing　tool　for　the　air　passenger　demand　forecasting.