In　general,　solving　prediction　problems　requires　a　series　of　operations　for　the　data　set　such　as　preprocessing,　partitioning,　and　structuring　features,　so　as　to　fit　a　better　prediction　model.　For　time　series　data,　it　is　divided　into　different　data　sets　according　to　certain　rules　to　achieve　the　effect　of　improving　the　accuracy　of　the　prediction　model.　This　paper　proposes　a　more　novel　clustering　method　which　the　traditional　Euclidean　distance　and　dynamic　time　planning　are　separately　weighted　and　combined　to　do　the　distance　calculation　method　in　clustering.　A　time　series　contains　both　a　time　dimension　and　a　spatial　dimension.　Euclidean　distance　is　mainly　used　for　spatial　distance　calculation.　Dynamic　time　warping　can　calculate　the　similarity　calculation　in　time　dimension,　similar　to　the　distance　calculation　in　the　spatial　dimension.　The　measure　of　similarity　of　time　series　is　a　measure　of　the　degree　of　similarity　between　two　time　series.　It　is　verified　by　experiments　that　under　the　same　prediction　model,　this　novel　clustering　method　is　better　than　the　Euclidean　distance　clustering　method　and　the　traditional　dynamic　time　warping　method.　©　2018　IEEE.