Due　to　the　fast　development　and　wide　deployment　of　cloud　computing　and　big　data,　applications　in　various　industries　have　shown　their　unique　advantages.　Currently,　many　fields　have　gained　great　changes　due　to　benefits　brought　by　big　data　analysis.　This　paper　accurately　and　quickly　analyzes　the　data　of　Ground-based　Wide-Angle　Camera　array　(GWAC)　based　on　Grubbs　and　detects　anomaly　astronomical　events.　In　this　paper,　we　improved　ARIMA　model　with　the　dynamic　and　parallel　processing.　The　model　identifies　anomaly　events　that　occur　in　light　curves　obtained　from　GWAC　as　early　as　possible　with　high　degree　of　confidence.　A　major　advantage　of　improved　ARIMA　is　that　it　can　dynamically　adjust　its　model　parameters　during　the　real-time　processing　of　the　time　series　data,　and　increase　its　efficiency　through　a　multi-process　parallel　approach.　We　identify　the　anomaly　points　based　on　the　Grubbs　and　improved　ARIMA　model.　Experimental　results　with　real　survey　data　show　that　the　improved　ARIMA　model　can　identify　the　anomaly　points　for　all　light　curves.　We　also　evaluate　our　model　with　simulated　anomaly　events　of　various　types　embedded　in　the　real　time　series　data.　The　improved　ARIMA　model　is　able　to　generate　the　early　warning　triggers　for　all　of　them.　These　results　from　the　experiments　demonstrate　that　the　proposed　improved　ARIMA　model　is　a　promising　method　for　real-time　anomaly　detection　of　short　time-scale　GWAC　light　curves.　(C)　2018　Elsevier　B.V.　All　rights　reserved.