Prediction　methods　have　become　a　hot　topic　in　intelligent　decision　making.　Most　of　the　existing　prediction　methods　focus　on　the　prediction　accuracy　and　stability.　As　a　second　choice,　accurate　interval　prediction　can　provide　a　relatively　reliable　reference　in　the　sense　of　probability　and　provide　help　for　assisting　decision　management.　Therefore,　we　propose　a　novel　interval　prediction　approach.　Firstly,　the　decomposition　method　based　on　ensemble　empirical　mode　decomposition　(EEMD)　is　utilized　to　alleviate　the　complexity　of　the　original　time　series,　thereby　generating　a　series　of　relatively　smooth　subseries.　Secondly,　a　three-way　clustering　(TWC)　algorithm　is　established　by　integrating　sample　entropy　into　probabilistic　rough　set,　enriching　the　three-way　clustering　theory　from　the　perspective　of　entropy.　Thirdly,　aiming　at　determining　the　optimal　input　dimensions　of　different　neural　networks,　the　feature　selection　technique　based　on　phase　space　reconstruction　(PSR)　is　constructed.　Furthermore,　an　interval　prediction　system　based　on　TWC　is　proposed　to　provide　a　new　data-driven　prediction　method.　Finally,　the　proposed　approach　is　applied　to　predict　the　interval　price　of　crude　oil.　On　the　one　hand,　the　practicability　of　the　constructed　prediction　approach　is　verified;　on　the　other　hand,　it　provides　a　new　theoretical　method　for　interval　prediction　of　crude　oil　price.　The　experiment　results　show　the　proposed　prediction　approach　can　assist　the　decision-makers　to　make　scientific　and　reasonable　decisions.　(C)　2022　Published　by　Elsevier　B.V.