Practitioners　and　policy　makers　rely　on　accurate　crude　oil　forecasting　to　avoid　price　risks　and　grasp　investment　opportunities,　but　the　core　of　existing　predictive　models　for　such　prices　is　based　on　point-valued　inputs　and　outputs,　which　may　suffer　from　informational　loss　of　volatility.　This　paper　addresses　this　issue　by　proposing　a　modified　threshold　autoregressive　interval-valued　models　with　interval-valued　factors　(MTARIX),　as　extended　by　Sun　et　al.　[Threshold　autoregressive　models　for　interval-valued　time　series.　J.　Econom.,　2018,　206,　414-446],　to　analyze　and　forecast　interval-valued　crude　oil　prices.　In　contrast　to　point-valued　data　methods,　MTARIX　models　simultaneously　capture　nonlinear　features　in　price　trend　and　volatility,　and　this　informational　gain　can　produce　more　accurate　forecasts.　Several　interval-valued　factors　and　point-valued　threshold　variables　are　analyzed,　including　supply　and　demand,　speculation,　stock　market,　monetary　market,　technical　factor,　and　search　query　data.　Empirical　results　suggest　that　MTARIX　models　with　appropriate　threshold　variables　outperform　other　competing　forecast　models　(ACIX,　CR-SETARX,　ARX,　and　VARX).　The　findings　indicate　that　oil　price　range　information　is　more　valuable　than　oil　price　level　information　in　forecasting　crude　oil　prices.