Traffic　forecasting　is　an　important　part　in　realising　intelligent　traffic　management,　which　helps　traffic　controllers　and　travellers　make　effective　decisions.　However,　traffic　forecasting　accuracy　is　often　affected　by　missing　traffic　data　due　to　hardware　and　software　failure.　Therefore,　accurate　prediction　based　on　incomplete　traffic　data　is　an　important　problem　as　well　as　a　challenge.　Though　many　approaches　recover　the　missing　values　before　prediction,　the　errors　from　the　data-filling　step　are　likely　to　cause　additional　bias　to　the　prediction　result.　Besides,　this　tactic　is　difficult　to　guarantee　the　timeliness　and　may　impede　real-time　prediction.　In　this　case,　a　traffic　forecasting　model　is　proposed　to　directly　predict　traffic　data　with　missing　values.　This　model　develops　tensor　formed　dynamic　mode　decomposition,　recording　the　dynamic　information　of　traffic　data　into　a　state　transition　tensor.　In　addition,　the　model　takes　low　rank　property　of　the　dynamic　tensor　and　the　similarity　of　temporal　variation　trend　into　consideration.　In　order　to　verify　the　effectiveness　and　the　robustness　of　the　proposed　model,　experiments　were　performed　on　two　real-world　time　series　datasets.　The　results　demonstrate　that　the　model　achieves　better　performance　on　forecasting　than　other　baseline　approaches　under　the　impact　of　missing　data.