Traffic　flow　prediction　plays　an　important　role　in　intelligent　transportation　applications,　such　as　traffic　control,　navigation,　path　planning,　etc.,　which　are　closely　related　to　people＇s　daily　life.　In　the　last　twenty　years,　many　traffic　flow　prediction　approaches　have　been　proposed.　However,　some　of　these　approaches　use　the　regression　based　mechanisms,　which　cannot　achieve　accurate　short-term　traffic　flow　predication.　While,　other　approaches　use　the　neural　network　based　mechanisms,　which　cannot　work　well　with　limited　amount　of　training　data.　To　this　end,　a　light　weight　tensor-based　traffic　flow　prediction　approach　is　proposed,　which　can　achieve　efficient　and　accurate　short-term　traffic　flow　prediction　with　continuous　traffic　flow　data　in　a　limited　period　of　time.　In　the　proposed　approach,　first,　a　tensor-based　traffic　flow　model　is　proposed　to　establish　the　multi-dimensional　relationships　for　traffic　flow　values　in　continuous　time　intervals.　Then,　a　CANDECOMP/PARAFAC　decomposition　based　algorithm　is　employed　to　complete　the　missing　values　in　the　constructed　tensor.　Finally,　the　completed　tensor　can　be　directly　used　to　achieve　efficient　and　accurate　traffic　flow　prediction.　The　experiments　on　the　real　dataset　indicate　that　the　proposed　approach　outperforms　many　current　approaches　on　traffic　flow　prediction　with　limited　amount　of　traffic　flow　data.