The　data　stream　processing　framework　processes　the　stream　data　based　on　event-time　to　ensure　that　the　request　can　be　responded　to　in　real-time.　In　reality,　streaming　data　usually　arrives　out-of-order　due　to　factors　such　as　network　delay.　The　data　stream　processing　framework　commonly　adopts　the　watermark　mechanism　to　address　the　data　disorderedness.　Watermark　is　a　special　kind　of　data　inserted　into　the　data　stream　with　a　timestamp,　which　helps　the　framework　to　decide　whether　the　data　received　is　late　and　thus　be　discarded.　Traditional　watermark　generation　strategies　are　periodic;　they　cannot　dynamically　adjust　the　watermark　distribution　to　balance　the　responsiveness　and　accuracy.　This　paper　proposes　an　adaptive　watermark　generation　mechanism　based　on　the　time　series　prediction　model　to　address　the　above　limitation.　This　mechanism　dynamically　adjusts　the　frequency　and　timing　of　watermark　distribution　using　the　disordered　data　ratio　and　other　lateness　properties　of　the　data　stream　to　improve　the　system　responsiveness　while　ensuring　acceptable　result　accuracy.　We　implement　the　proposed　mechanism　on　top　of　Flink　and　evaluate　it　with　real-world　datasets.　The　experiment　results　show　that　our　mechanism　is　superior　to　the　existing　watermark　distribution　strategies　in　terms　of　both　system　responsiveness　and　result　accuracy.