The　purpose　of　early　classification　of　time　series　is　to　predict　the　class　label　of　time　series　in　advance　when　time　series　has　not　been　collected　completely,　which　is　meaningful　in　financial　fields　with　high　timeliness　requirements.　Current　financial　analysis　techniques,　such　as　methods　based　on　the　Support　Vector　Machine　and　Naive　Bayes,　need　to　analyze　complete　data　to　get　results,　which　may　delay　managers　to　supervise　the　market　therefore,　we　propose　decision-making　method　of　futures　trading　using　dictionary-based　early　classification　of　time　series.　Specifically,　we　train　a　group　of　basic　classifiers　under　different　timestamps　the　classifier　extract　subsequences　along　the　sliding　window　to　construct　the　bag-of-pattern,　and　then　use　the　logistic　regression　model　for　classification.　In　addition,　considering　that　the　main　task　of　early　classification　of　time　series　is　to　determine　the　earliest　time　of　reliable　classification.　Thus,　based　on　the　idea　of　dynamic　decision　fusion,　we　combine　the　number　of　classifiers,　prediction　results　of　different　classifiers,　and　the　conflict　function　value　between　earliness　and　accuracy　of　results　and　select　the　best　number　of　classifiers　and　the　threshold　of　reliability,　which　determine　the　time　of　reliable　output.　Consequently,　we　obtain　an　algorithm　for　finding　the　earliest　time　of　reliable　classification.　Experimental　results　on　different　futures　datasets　show　that,　compared　with　the　current　popular　financial　analysis　technology,　in　the　aspect　of　earliness,　we　use　early　classification　of　time　series　to　classify　the　futures　data　only　by　seeing　about　60%　of　length　of　the　complete　futures　data,　which　helps　the　manager　of　financial　regulatory　authorities　to　start　making　decisions　about　40%　earlier,　leaving　more　time　for　judging　and　guiding　decisions.　In　terms　of　accuracy,　our　method　has　achieved　better　performance.　©　2021　ACM.